Neural_Network_Feature_Engineering.ipynb

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      "source": [
        "pip install forcats"
      ],
      "execution_count": 2,
      "outputs": [
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          "text": [
            "Collecting forcats\n",
            "  Downloading https://files.pythonhosted.org/packages/81/dd/1b39ae32e2238bda78918bd50e0c2096cd28b5370735fc8ef249298ce5ab/forcats-0.0.1.tar.gz\n",
            "Collecting siuba\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/11/60/2e8ad9962ce06a7bf8356b3018342b55d12ae86df5c9bd4aa7aa7670f9e8/siuba-0.0.24-py3-none-any.whl (104kB)\n",
            "\u001b[K     |████████████████████████████████| 112kB 8.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from forcats) (1.1.5)\n",
            "Requirement already satisfied: SQLAlchemy>=1.1.18 in /usr/local/lib/python3.6/dist-packages (from siuba->forcats) (1.3.20)\n",
            "Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from siuba->forcats) (1.18.5)\n",
            "Requirement already satisfied: PyYAML>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from siuba->forcats) (3.13)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->forcats) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas->forcats) (2.8.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas->forcats) (1.15.0)\n",
            "Building wheels for collected packages: forcats\n",
            "  Building wheel for forcats (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for forcats: filename=forcats-0.0.1-cp36-none-any.whl size=1546 sha256=bd89aae4e10e8f33bc2fb290bc9dad8017d2ee6a570bf599228741c7931b0128\n",
            "  Stored in directory: /root/.cache/pip/wheels/ce/c3/b9/17594d5fa27e0ca37e60a31725adc79966b81c88772bd43516\n",
            "Successfully built forcats\n",
            "Installing collected packages: siuba, forcats\n",
            "Successfully installed forcats-0.0.1 siuba-0.0.24\n"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from scipy.stats import norm\n",
        "from scipy import stats\n",
        "from scipy.stats import ttest_ind\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "from siuba.dply.forcats import fct_lump, fct_reorder \n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 3,
      "outputs": []
    },
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        "ks = pd.read_csv('ks_combo.csv')"
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      "execution_count": 4,
      "outputs": []
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        "ks.shape"
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              "(702411, 13)"
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        "ks.head()"
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              "           ID  ... usd.pledged\n",
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      "source": [
        "ks.tail()"
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              "      <td>USD</td>\n",
              "      <td>2014-10-17 02:35:30</td>\n",
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              "      <td>999977640</td>\n",
              "      <td>The Tribe</td>\n",
              "      <td>Narrative Film</td>\n",
              "      <td>Film &amp; Video</td>\n",
              "      <td>USD</td>\n",
              "      <td>2011-07-19 03:35:14</td>\n",
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              "      <td>999986353</td>\n",
              "      <td>Walls of Remedy- New lesbian Romantic Comedy f...</td>\n",
              "      <td>Narrative Film</td>\n",
              "      <td>Film &amp; Video</td>\n",
              "      <td>USD</td>\n",
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              "      <td>15000</td>\n",
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              "      <td>20</td>\n",
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              "      <td>1</td>\n",
              "      <td>US</td>\n",
              "      <td>20</td>\n",
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              "    <tr>\n",
              "      <th>702409</th>\n",
              "      <td>999987933</td>\n",
              "      <td>BioDefense Education Kit</td>\n",
              "      <td>Technology</td>\n",
              "      <td>Technology</td>\n",
              "      <td>USD</td>\n",
              "      <td>2016-02-13 02:00:00</td>\n",
              "      <td>15000</td>\n",
              "      <td>2016-01-13 18:13:53</td>\n",
              "      <td>200</td>\n",
              "      <td>failed</td>\n",
              "      <td>6</td>\n",
              "      <td>US</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>702410</th>\n",
              "      <td>999988282</td>\n",
              "      <td>Nou Renmen Ayiti!  We Love Haiti!</td>\n",
              "      <td>Performance Art</td>\n",
              "      <td>Art</td>\n",
              "      <td>USD</td>\n",
              "      <td>2011-08-16 09:07:47</td>\n",
              "      <td>2000</td>\n",
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              "      <td>524</td>\n",
              "      <td>failed</td>\n",
              "      <td>17</td>\n",
              "      <td>US</td>\n",
              "      <td>524</td>\n",
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              "               ID  ... usd.pledged\n",
              "702406  999976400  ...          25\n",
              "702407  999977640  ...         155\n",
              "702408  999986353  ...          20\n",
              "702409  999987933  ...         200\n",
              "702410  999988282  ...         524\n",
              "\n",
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      },
      "source": [
        "ks['state'].value_counts()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "failed                 365940\n",
              "successful             247037\n",
              "canceled                71133\n",
              "live                     7227\n",
              "undefined                7117\n",
              "                        ...  \n",
              "4262                        1\n",
              "2015-09-08 22:08:39         1\n",
              "7271                        1\n",
              "3671                        1\n",
              "8609.6                      1\n",
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          "execution_count": 8
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        "id": "Tnh_DJorpYu2",
        "outputId": "23f616c3-dff8-49d6-c01a-c90eb548d505"
      },
      "source": [
        "ks['main_category'].value_counts()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Film & Video    121264\n",
              "Music            98662\n",
              "Publishing       74107\n",
              "Games            63239\n",
              "Technology       58697\n",
              "                 ...  \n",
              " pants               1\n",
              " Demons              1\n",
              "Textiles             1\n",
              "Metal                1\n",
              "Vegan                1\n",
              "Name: main_category, Length: 120, dtype: int64"
            ]
          },
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          "execution_count": 9
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      "cell_type": "code",
      "metadata": {
        "id": "lmOeTSkPpYu2"
      },
      "source": [
        "ks['main_category'] = fct_lump(ks['main_category'], n= 11)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
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      },
      "source": [
        "ks['main_category'].value_counts().plot(kind='barh', figsize=(14,6), title='Project Categories')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa19bf7df60>"
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          "execution_count": 12
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          "output_type": "display_data",
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ft/7/AZwwxjHeCry3qh4OnAWc2rduMfA44BnAiqk+OUmSJEmC6RXCbq+qpe31bCDAPydZDXwO2Au4P7AGeHKSNyR5fFVt6NvHR9vPlcDQGMd4NPD+tnwmvdA14ryq2lRVV7Xj/JYkxyUZTjK88bYNY3WRJEmSpM2aTiFstKOBPYCD271iNwA7VtU1wEH0wtg/JfmHvm3ubD83MvH73e7sW85YHarqjKpaVlXL5u28aIK7lyRJkqTpHcIWATdW1V1J/hDYByDJnsBtVfWfwBvpBbKt9TXgqLZ8NHDRFNYrSZIkSVs0nZ6OONpZwMeTrAGGgW+19gOANybZBNwFvHQC+zweeHeSvwZuAo6dwnolSZIkaYtSVYOuYUZasHhJLT7m5EGXIakD61YsH3QJkiRpBkqysqqWjW6fztMRJUmSJGnWmc7TEae1A/ZaxLD/d1ySJEnSBDkSJkmSJEkdMoRJkiRJUocMYZIkSZLUIUOYJEmSJHXIECZJkiRJHTKESZIkSVKHDGGSJEmS1CFDmCRJkiR1yBAmSZIkSR0yhEmSJElShwxhkiRJktQhQ5gkSZIkdcgQJkmSJEkdmj/oAmaqNes3MHTiBYMuQ9IstW7F8kGXIEmSthNHwiRJkiSpQ4YwSZIkSerQjA9hSTYmWdX3GtrG/Q0luWJqqpMkSZKku5sN94TdXlVLB12EJEmSJG2NGT8SNpYkS5NcnGR1knOT3HsL7QcnuTzJ5cBfDrR4SZIkSbPabAhhO/VNRTy3tb0PeHVVPRxYA7x2C+3vBo6vqgO7LFySJEnS3DPrpiMmWQTsVlVfak3vBc7ZTPturf3Lrf1M4OljHSjJccBxAPN23WPqz0SSJEnSrDcbRsI6U1VnVNWyqlo2b+dFgy5HkiRJ0gw060JYVW0Afpbk8a3pBcCXNtN+M3Bzkse19qO7rViSJEnSXDIbpiOO5Rjg9CQ7A9cBx26h/VjgXUkKuLDrYiVJkiTNHTM+hFXVwjHaVgGPmkD7SqD/oRx/M5U1SpIkSdKIWTcdUZIkSZKmM0OYJEmSJHVoxk9HHJQD9lrE8Irlgy5DkiRJ0gzjSJgkSZIkdcgQJkmSJEkdMoRJkiRJUocMYZIkSZLUIUOYJEmSJHXIECZJkiRJHTKESZIkSVKHDGGSJEmS1CFDmCRJkiR1yBAmSZIkSR0yhEmSJElShwxhkiRJktSh+YMuYKZas34DQydeMOgyJKlT61YsH3QJkiTNeI6ESZIkSVKHDGGSJEmS1CFDmCRJkiR1aMaFsCR7J/lYkmuTfCfJKUl2SLI0yeF9/U5KcsIga5UkSZKk0WZUCEsS4KPAeVW1BPg9YCHwemApcPhmNp/oseZN1b4kSZIkacSMCmHAE4E7qurdAFW1EXgV8GfAvwLPS7IqyfNa//2SfDHJdUlePrKTJH+S5JLW9/+NBK4ktyb5tySXA4/u9MwkSZIkzQkzLYQ9DFjZ31BVPwfWAf8EfLCqllbVB9vqfYGnAocAr01yzyS/DzwPeGxVLQU2Ake3/vcCvl5VB1bVV0YfPMlxSYaTDG+8bcN2OD1JkiRJs91s/zthF1TVncCdSW4E7g88CTgYuLQ3u5GdgBtb/43AR8bbWVWdAZwBsGDxktqOdUuSJEmapWZaCLsKOLK/IcmuwAOAX43R/86+5Y30zjfAe6vqb8fof0eb4ihJkiRJ28VMm474eWDnJC+EXz8849+A9wA3ALts5T6OTHK/to/7JNln+5QrSZIkSXc3o0JYVRXwbOCPk1wLXAPcAfxf4L/oPYij/8EcY+3jKuDvgAuTrAY+Cyze7sVLkiRJEjNvOiJV9X3gmWOsuhN45Ga2279v+YPAB8fos3AqapQkSZKk8cyokTBJkiRJmulm3EjYdHHAXosYXrF80GVIkiRJmmEcCZMkSZKkDhnCJEmSJKlDhjBJkiRJ6pAhTJIkSZI6ZAiTJEmSpA4ZwiRJkiSpQ4YwSZIkSeqQIUySJEmSOmQIkyRJkqQOGcIkSZIkqUOGMEmSJEnqkCFMkiRJkjo0f9AFzFRr1m9g6MQLBl2GJHVq3Yrlgy5BkqQZz5EwSZIkSeqQIUySJEmSOmQIkyRJkqQOzZkQluRZSSrJvuOs3y3JX3RdlyRJkqS5Zc6EMOD5wFfaz7tJMh/YDTCESZIkSdqu5kQIS7IQeBzwYuCo1nZokouSnA9cBawAHpxkVZI3Dq5aSZIkSbPZXHlE/RHAp6vqmiQ/SXJwaz8I2L+q1iYZastLx9tJkuOA4wDm7brHdi5ZkiRJ0mw0J0bC6E1B/EBb/gC/mZJ4SVWt3dqdVNUZVbWsqpbN23nRVNcoSZIkaQ6Y9SNhSe4DPBE4IEkB84ACLgB+McjaJEmSJM09c2Ek7EjgzKrap6qGqup3gbXA40f1uwXYpfPqJEmSJM0pcyGEPR84d1TbRxj1lMSq+gnw1SRX+GAOSZIkSdvLrJ+OWFV/OEbbqcCpY7T/r06KkiRJkjRnzYWRMEmSJEmaNmb9SNj2csBeixhesXzQZUiSJEmaYRwJkyRJkqQOGcIkSZIkqUOGMEmSJEnqkCFMkiRJkjpkCJMkSZKkDhnCJEmSJKlDhjBJkiRJ6pAhTJIkSZI6ZAiTJEmSpA4ZwiRJkiSpQ4YwSZIkSeqQIUySJEmSOmQIkyRJkqQOzR90ATPVmvUbGDrxgkGXIUnT2roVywddgiRJ044jYZIkSZLUIUOYJEmSJHVoWoawJBuTrEpyZZLLk/yfJJOqNcknk+w21TVKkiRJ0mRM13vCbq+qpQBJ7ge8H9gVeO1Ed1RVh09xbZIkSZI0adNyJKxfVd0IHAe8LD3zkrwxyaVJVif53wBJFif5chtBuyLJ41v7uiS7t+W/T3J1kq8kOTvJCa39i0nekOSSJNeMbCtJkiRJU226joTdTVVdl2QecD/gCGBDVT0yyQLgq0kuBJ4DfKaqXt/67ty/jySPBJ4LHAjcE7gMWNnXZX5VHZLkcHojboeNriPJcfQCIfN23WOqT1OSJEnSHDAjQtgoTwEenuTI9n4RsAS4FHhXknsC51XVqlHbPRb4WFXdAdyR5OOj1n+0/VwJDI114Ko6AzgDYMHiJbWtJyJJkiRp7pkRISzJg4CNwI1AgOOr6jNj9HsCsBx4T5I3V9X7JnCYO9vPjcyQz0WSJEnSzDPt7wlLsgdwOnBaVRXwGeClbcSLJL+X5F5J9gFuqKq3A+8ADhq1q68Cz0yyY5KFwDO6OwtJkiRJ6pmuIz47JVlF796tXwFnAm9u695Bb7rgZUkC3AQ8CzgU+OskdwG3Ai/s32FVXZrkfGA1cAOwBtiw3c9EkiRJkvqkN7g0NyRZWFW3JtkZ+DJwXFVdNpl9LVi8pBYfc/LUFihJs8y6FcsHXYIkSQOTZGVVLRvdPl1HwraXM5LsB+wIvHeyAUySJEmSJmtOjYRNpWXLltXw8PCgy5AkSZI0TY03EjbtH8whSZIkSbOJIUySJEmSOmQIkyRJkqQOGcIkSZIkqUOGMEmSJEnqkCFMkiRJkjpkCJMkSZKkDhnCJEmSJKlDhjBJkiRJ6pAhTJIkSZI6ZAiTJEmSpA4ZwiRJkiSpQ4YwSZIkSerQ/EEXMFOtWb+BoRMvGHQZkqRR1q1YPugSJEnaLEfCJEmSJKlDhjBJkiRJ6tA2hbAk902yqr1+lGR93/sdtmL7Q5N8Yltq6NvXi5KcNhX7kiRJkqTtZZvuCauqnwBLAZKcBNxaVW+agrokSZIkaVaa8umISQ5O8qUkK5N8Jsni1v6QJJ9LcnmSy5I8uG2yMMmHk3wryVlJ0vqvS/K61ndNkn1b+32SnJdkdZKLkzx8jBqGknyh9fl8kge09ge3bdYk+ackt7b29yV5Vt/2ZyU5Yqo/G0mSJEma6hAW4K3AkVV1MPAu4PVt3VnA26rqQOAxwA9b+yOAVwL7AQ8CHtu3vx9X1UHAfwAntLbXAd+oqocD/xd43xh1vBV4b+tzFnBqaz8FOKWqDgCu7+v/TuBFAEkWtfp+69GHSY5LMpxkeONtG7b8aUiSJEnSKFMdwhYA+wOfTbIK+Dtg7yS7AHtV1bkAVXVHVd3Wtrmkqq6vqk3AKmCob38fbT9X9rU/Djiz7ecLwH2T7DqqjkcD72/LZ7ZtRtrPacsj66mqLwFLkuwBPB/4SFX9avTJVdUZVbWsqpbN23nR1nwekiRJknQ3U/13wgJcWVWPvltjL4SN586+5Y2jarpznPbt4X3AnwBHAcdu52NJkiRJmqOmeiTsTmCPJI8GSHLPJA+rqluA60fuu0qyIMnOkzzGRcDRbT+H0puy+PNRfb5GL0zR+l7Uli8GntuWjxq1zXvoTYukqq6aZG2SJEmStFlTHcI2AUcCb0hyOb3phY9p614AvDzJanoh6XcmeYyTgIPbflYAx4zR53jg2NbnBcArWvsrgb9q7Q8Bfn1jV1XdAHwTePck65IkSZKkLUpVDbqGzrTRt9urqpIcBTy/qo7oW7cGOKiqtvjUjQWLl9TiY07evgVLkiZs3Yrlgy5BkiQAkqysqmWj27f3fVbTzcHAae0x+DcDfwqQ5DB6T0h8y9YEMEmSJEmarDk1EjaVli1bVsPDw4MuQ5IkSdI0Nd5I2JT/sWZJkiRJ0vgMYZIkSZLUIUOYJEmSJHXIECZJkiRJHTKESZIkSVKHDGGSJEmS1CFDmCRJkiR1yBAmSZIkSR0yhEmSJElShwxhkiRJktQhQ5gkSZIkdcgQJkmSJEkdMoRJkiRJUofmD7qAmWrN+g0MnXjBoMuQJA3YuhXLB12CJGmGcSRMkiRJkjpkCJMkSZKkDk3LEJbk/knen+S6JCuT/HeSZw+6LkmSJEnaVtMuhCUJcB7w5ap6UFUdDBwF7D3YyiRJkiRp2027EAY8EfhlVZ0+0lBV362qtyYZSnJRksva6zEASQ5N8qUkH2ujZyuSHJ3kkiRrkjy49dsjyUeSXNpej23tf5BkVXt9I8kuAzlzSZIkSbPedHw64sOAy8ZZdyPw5Kq6I8kS4GxgWVt3IPD7wE+B64B3VNUhSV4BHA+8EjgFeEtVfSXJA4DPtG1OAP6yqr6aZCFwx3Y6N0mSJElz3HQMYXeT5G3A44BfAocBpyVZCmwEfq+v66VV9cO2zXeAC1v7GuAP2/JhwH69GY8A7NpC11eBNyc5C/hoVV0/Ti3HAccBzNt1j6k5QUmSJElzynQMYVcCzx15U1V/mWR3YBh4FXADvVGve3D3Eas7+5Y39b3fxG/O8x7Ao6pq9EjXiiQXAIcDX03y1Kr61ujCquoM4AyABYuX1OROT5IkSdJcNh3vCfsCsGOSl/a17dx+LgJ+WFWbgBcA8ya47wvpTU0EoI2okeTBVbWmqt4AXArsO9niJUmSJGlzpl0Iq6oCngX8QZK1SS4B3gu8Gvh34Jgkl9MLSr+Y4O5fDixLsjrJVcBLWvsrk1yRZDVwF/CpqTgXSZIkSRotvcyjiVqweEktPubkQZchSRqwdSuWD7oESdI0lWRlVS0b3T7tRsIkSZIkaTabjg/mmBEO2GsRw/7fT0mSJEkT5EiYJEmSJHXIECZJkiRJHTKESZIkSVKHDGGSJEmS1CFDmCRJkiR1yBAmSZIkSR0yhEmSJElShwxhkiRJktQhQ5gkSZIkdcgQJkmSJEkdMoRJkiRJUocMYZIkSZLUIUOYJEmSJHVo/qALmKnWrN/A0IkXDLoMSZKmhXUrlg+6BEmaMRwJkyRJkqQOGcIkSZIkqUNTHsKSbEyyKskVSc5JsvMW+q9LsvsY7SclOaEt/2OSwzazj/ckOXKM9j2TfHgy5yFJkiRJ28P2GAm7vaqWVtX+wC+Bl2zrDqvqH6rqc5PY7gdV9VvhTJIkSZIGZXtPR7wIeEiSQ5N8YqQxyWlJXtTX72+SrElySZKHjN5J/0hXkhVJrkqyOsmb+ro9IcnXklzX13coyRVt+UVJPprk00muTfKvfft/cZJr2vHfnuS0qf0YJEmSJKlnuz0dMcl84OnAp7ei+4aqOiDJC4GTgWeMs8/7As8G9q2qSrJb3+rFwOOAfYHzgbGmIS4FHgHcCVyd5K3ARuDvgYOAW4AvAJdvRc2SJEmSNGHbYyRspySrgGHge8acdwcAAA1ESURBVMA7t2Kbs/t+Pnoz/TYAdwDvTPIc4La+dedV1aaqugq4/zjbf76qNlTVHcBVwD7AIcCXquqnVXUXcM54B09yXJLhJMMbb9uwFaclSZIkSXe3PUbCbq+qpf0NSX7F3QPfjqO2qXGW796p6ldJDgGeBBwJvAx4Ylt9Z/8hx9lFf5+NTPD8q+oM4AyABYuXjFunJEmSJI2nq0fUfxfYL8mCNoXwSaPWP6/v53+Pt5MkC4FFVfVJ4FXAgVNQ26XAHyS5d5tC+dwp2KckSZIkjWm73RPWr6q+n+RDwBXAWuAbo7rcO8lqeiNVz9/MrnYBPpZkR3qjXX81BbWtT/LPwCXAT4Fv0Zv2KEmSJElTLlXOqkuysKpubSNh5wLvqqpzN7fNgsVLavExJ3dToCRJ09y6FcsHXYIkTTtJVlbVstHtXU1HnO5Oag8TGRmpO2/A9UiSJEmapTqZjjjdVdUJg65BkiRJ0txgCJukA/ZaxLBTLyRJkiRNkNMRJUmSJKlDhjBJkiRJ6pAhTJIkSZI6ZAiTJEmSpA4ZwiRJkiSpQ4YwSZIkSeqQIUySJEmSOmQIkyRJkqQOGcIkSZIkqUOGMEmSJEnqkCFMkiRJkjpkCJMkSZKkDs0fdAEz1Zr1Gxg68YJBlyFJkjSrrVuxfNAlSFPOkTBJkiRJ6pAhTJIkSZI6ZAiTJEmSpA7NqBCWpJL8Z9/7+UluSvKJSe7vHUn2m7oKJUmSJGnzZtqDOX4B7J9kp6q6HXgysH6yO6uqP5uyyiRJkiRpK8yokbDmk8DIY3KeD5w9siLJSUlO6Ht/RZKhJPdKckGSy1vb89r6LyZZ1pafluSy1ufzHZ6PJEmSpDlkJoawDwBHJdkReDjw9a3Y5mnAD6rqwKraH/h0/8okewBvB55bVQcCfzzWTpIcl2Q4yfDG2zZs00lIkiRJmptmXAirqtXAEL1RsE9u5WZrgCcneUOSx1fV6AT1KODLVbW2HeOn4xz7jKpaVlXL5u28aHInIEmSJGlOm3EhrDkfeBN9UxGbX3H3c9oRoKquAQ6iF8b+Kck/dFGkJEmSJI02U0PYu4DXVdWaUe3r6IUtkhwEPLAt7wncVlX/CbxxpE+fi4EnJBnpf5/tV7okSZKkuWymPR0RgKq6Hjh1jFUfAV6Y5Ep694pd09oPAN6YZBNwF/DSUfu7KclxwEeT3AO4kd6TFyVJkiRpSs2oEFZVC8do+yLwxbZ8O/CUMTZdB3xmjG0P7Vv+FPCpKSlUkiRJksYxU6cjSpIkSdKMNKNGwqaTA/ZaxPCK5VvuKEmSJEl9HAmTJEmSpA4ZwiRJkiSpQ4YwSZIkSeqQIUySJEmSOmQIkyRJkqQOGcIkSZIkqUOGMEmSJEnqkCFMkiRJkjpkCJMkSZKkDhnCJEmSJKlDhjBJkiRJ6pAhTJIkSZI6NH/QBcxUa9ZvYOjECwZdhiRJkiRg3Yrlgy5hqzkSJkmSJEkdMoRJkiRJUocMYZIkSZLUoa0KYUk2JlnV9xpK8rW2bijJFdtSRJI/T3J1kiuT/MU4ff4gyX+Papuf5IYkeyb5ZJLdxtjupCQnbEt9kiRJkjRVtvbBHLdX1dJRbY+ZigKSzAdeDzwEuAV4wDhdLwL2TrJPVX23tR0GXFlVPwAOn4p6JEmSJGl7mvR0xCS3jtH2oiTnJflsknVJXpbkr5J8I8nFSe4zzu7mA/etnu+O1aGqNgEfAo7qaz4KOLsde12S3dvya5Jck+QrwEP76ntwkk8nWZnkoiT7tvahJF9IsjrJ55OMFwQlSZIkaZtsbQjbqW8q4rlb6Ls/8BzgkfRGuG6rqkcA/w28cIz+84HLgfM2E9JGnE0LYUkW0Bv9+kh/hyQHtz5L2/pH9q0+Azi+qg4GTgD+vbW/FXhvVT0cOAs4dayDJzkuyXCS4Y23bdhCqZIkSZL027ZlOuJ4/quqbgFuSbIB+HhrXwM8fIz+/wK8uy2fn+QpwHLgf1TV3e7lqqrhJAuTPBT4feDrVfXTUft7PHBuVd0GkOT89nMhvSmU5yQZ6bug/Xw0veAIcCbwr2OdWFWdQS/IsWDxkhr/I5AkSZKksW2PP9Z8Z9/ypr73m8Y53lOBU6pqXZL7AecAvwDeOM7+R0bDfr8tb617ADdPIExKkiRJ0pSbDo+o/wa/mab4ZmAX4GHAynH6nw38CfBE4GNjrP8y8KwkOyXZBXgmQFX9HFib5I8B0nNg2+Zr/OZes6PpPQREkiRJkqbcdAhhrwSWJrkSuAT4DHAp8JaxOlfVN+mNlH2hqn4xxvrLgA/Su8/sU21fI44GXpzkcuBK4IjWfjxwbJLVwAuAV0zBeUmSJEnSb0mVtzZNxoLFS2rxMScPugxJkiRJwLoVywddwm9JsrKqlo1unw4jYZIkSZI0Z2yPB3PMCQfstYjhaZi2JUmSJE1vjoRJkiRJUocMYZIkSZLUIUOYJEmSJHXIECZJkiRJHTKESZIkSVKHDGGSJEmS1CH/WPMkJbkFuHrQdWi72x348aCLUCe81nOH13pu8DrPHV7ruWMmXut9qmqP0Y3+nbDJu3qsv36t2SXJsNd5bvBazx1e67nB6zx3eK3njtl0rZ2OKEmSJEkdMoRJkiRJUocMYZN3xqALUCe8znOH13ru8FrPDV7nucNrPXfMmmvtgzkkSZIkqUOOhEmSJElShwxhE5TkaUmuTvLtJCcOuh5tWZLfTfJfSa5KcmWSV7T2+yT5bJJr2897t/YkObVd49VJDurb1zGt/7VJjulrPzjJmrbNqUnS/ZlqRJJ5Sb6R5BPt/QOTfL1dnw8m2aG1L2jvv93WD/Xt429b+9VJntrX7u+AaSLJbkk+nORbSb6Z5NF+r2efJK9qv7uvSHJ2kh39Ts8OSd6V5MYkV/S1bffv8HjH0PYzzrV+Y/v9vTrJuUl261s3oe/rZH4nDFxV+drKFzAP+A7wIGAH4HJgv0HX5WuL120xcFBb3gW4BtgP+FfgxNZ+IvCGtnw48CkgwKOAr7f2+wDXtZ/3bsv3busuaX3Ttn36oM97Lr+AvwLeD3yivf8QcFRbPh14aVv+C+D0tnwU8MG2vF/7fi8AHti+9/P8HTC9XsB7gT9ryzsAu/m9nl0vYC9gLbBTe/8h4EV+p2fHC3gCcBBwRV/bdv8Oj3cMX51f66cA89vyG/qu9YS/rxP9nTAdXo6ETcwhwLer6rqq+iXwAeCIAdekLaiqH1bVZW35FuCb9P7FfgS9/4ij/XxWWz4CeF/1XAzslmQx8FTgs1X106r6GfBZ4Glt3a5VdXH1vuXv69uXOpZkb2A58I72PsATgQ+3LqOv9cg/Ax8GntT6HwF8oKrurKq1wLfpff/9HTBNJFlE71/q7wSoql9W1c34vZ6N5gM7JZkP7Az8EL/Ts0JVfRn46ajmLr7D4x1D28lY17qqLqyqX7W3FwN7t+UJfV8n+e/5gTOETcxewPf73l/f2jRDtGHoRwBfB+5fVT9sq34E3L8tj3edN9d+/RjtGoyTgb8BNrX39wVu7vtF3399fn1N2/oNrf9E/xlQ9x4I3AS8O72pp+9Ici/8Xs8qVbUeeBPwPXrhawOwEr/Ts1kX3+HxjqHB+VN6o5Uw8Ws9mX/PD5whTHNGkoXAR4BXVtXP+9e1/0vmo0JnuCTPAG6sqpWDrkXb3Xx6U1v+o6oeAfyC3rSiX/N7PfO1e3WOoBe69wTuBTxtoEWpM118h/09MXhJXgP8Cjhr0LV0yRA2MeuB3+17v3dr0zSX5J70AthZVfXR1nxDm65A+3ljax/vOm+ufe8x2tW9xwJ/lGQdvWkKTwROoTdtZX7r0399fn1N2/pFwE+Y+D8D6t71wPVV9fX2/sP0Qpnf69nlMGBtVd1UVXcBH6X3Pfc7PXt18R0e7xjqWJIXAc8Ajm6BGCZ+rX/CxH8nDJwhbGIuBZa0J7DsQO8Gv/MHXJO2oM39fSfwzap6c9+q84GRpygdA3ysr/2F7UlMjwI2tGkLnwGekuTe7f/OPgX4TFv38ySPasd6Yd++1KGq+tuq2ruqhuh9P79QVUcD/wUc2bqNvtYj/wwc2fpXaz+qPVXpgcASejd4+ztgmqiqHwHfT/LQ1vQk4Cr8Xs823wMelWTndh1GrrPf6dmri+/weMdQh5I8jd7tA39UVbf1rZrQ97V9xyf6O2Hwunj6x2x60Xs6zzX0ns7ymkHX42urrtnj6E01WA2saq/D6c0J/jxwLfA54D6tf4C3tWu8BljWt68/pXeD6LeBY/valwFXtG1Oo/0hdF8Dve6H8punIz6I3i/wbwPnAAta+47t/bfb+gf1bf+adj2vpu+peP4OmD4vYCkw3L7b59F7Mprf61n2Al4HfKtdizPpPTHN7/QseAFn07vX7y56o9sv7uI7PN4xfHV+rb9N736tkf82O72v/4S+r5P5nTDo18g/jJIkSZKkDjgdUZIkSZI6ZAiTJEmSpA4ZwiRJkiSpQ4YwSZIkSeqQIUySJEmSOmQIkyRJkqQOGcIkSZIkqUOGMEmSJEnq0P8HWjXQ1E8FGEYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tEJhynL-pYu3"
      },
      "source": [
        "ks['currency'] = fct_lump(ks['currency'], n= 11)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "bhwoF-XxpYu4",
        "outputId": "04f6a512-b0d0-42c3-f6d6-7cf092fa497a"
      },
      "source": [
        "ks['currency'].value_counts().plot(kind='barh', figsize=(14,6), title='Currencies')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa19bfd9c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HVNXxy6oYAADgFtpv8OnuTvKzSX6uqq5K8qkk30ryW0k+lPmXGUy+3GCxY3wiycuSvL+qLk1yfpJjZlA/AADAfh3QMz7d/dkk/2qRrpuS/Og+9rv/xPq5Sc5dZMzhB1IDAADAch3QLzAFAABYy1birW4r4qRjN2Vux9bVLgMAAFiDzPgAAACjJ/gAAACjJ/gAAACjJ/gAAACjJ/gAAACjJ/gAAACjJ/gAAACjJ/gAAACjJ/gAAACjJ/gAAACjJ/gAAACjJ/gAAACjd8hqF3CgLrt2dzZvP+9727t2bF3FagAAgLXEjA8AADB6gg8AADB6gg8AADB6yw4+VdVV9aqJ7RdX1enD+vuq6uKJ5fNV9ZGh76yq+kxVXVJVn6qqN1fVcVP/JAAAAEuYZsbnpiRPrKqjFnZ096O7++TuPjnJw5LckORlE0N+vbsfmOQ+ST6W5INVddspagEAAFjSNMHnu0l2Jnnhfsa9Nsl7uvv8hR0979VJvpjkMVPUAgAAsKRpn/E5M8nTqmrTYp1V9cQkW5K8ZD/H+WiSExfZf1tVzVXV3J4bd09ZKgAAsF5NFXy6+4Ykb07y/IV9VXVs5md7fqG7b9rPoWqJ4+/s7i3dvWXDxkWzFQAAwH7N4q1ur0ny7CQ/uLehqirJ2Ul2dPcnDuAYD0pyxQxqAQAA+D5TB5/u/mqSP898+NnrxUm+1d1n7mvfmvf8JMckee+0tQAAACxmVr/H51VJJt/u9ookP7LgldYfmuj//aq6JMmnkvxoktO6+9szqgUAAOBmDlnujt19+MT6l5JsnNg+dB/7PWu55wQAAFiOWc34AAAAHLSWPeNzazvp2E2Z27F1tcsAAADWIDM+AADA6Ak+AADA6Ak+AADA6Ak+AADA6Ak+AADA6Ak+AADA6Ak+AADA6Ak+AADA6Ak+AADA6Ak+AADA6Ak+AADA6Ak+AADA6Ak+AADA6B2y2gUcqMuu3Z3N28+7WduuHVtXqRoAAGAtMeMDAACMnuADAACM3syCT1W9tKour6pLq+riqvoXVXVBVV05bF9cVW8fxp5eVS8e1g+rqvOr6vRZ1QIAADBpJs/4VNWPJ3lckgd3901VdVSS2w7dT+vuuSX2u22SdyS5qLtPn0UtAAAAC83q5QbHJLm+u29Kku6+Pkmqan/nPjfJVd29fUZ1AAAAfJ9Z3er2/iR3r6pPVdUfVtVPTvT96cStbr8/0f4bSb7d3S9Y6qBVta2q5qpqbs+Nu2dUKgAAsN7MZManu79eVack+YkkpyU5t6r2zuIsdavb3yZ5aFX9cHd/aonj7kyyM0kOPeaEnkWtAADA+jOz3+PT3XuSXJDkgqq6LMkz97PL3yQ5O8lfVdXDu/sLs6oFAABg0kxudauq+1TVCRNNJyf5h/3t193vSPJfkry3qu44i1oAAAAWmtWMz+FJ/usQXr6b5Ook25K8PfPP+HxzGHd9dz9ycsfufkNVHZ3kXVX1qO7+1oxqAgAASDK7Z3wuSvLQRbpOXWL86Ytsn77YWAAAgGnN7BeYAgAAHKxm9nKDlXbSsZsyt2PrapcBAACsQWZ8AACA0RN8AACA0RN8AACA0RN8AACA0RN8AACA0RN8AACA0RN8AACA0RN8AACA0RN8AACA0RN8AACA0RN8AACA0RN8AACA0RN8AACA0TtktQs4UJdduzubt583s+Pt2rF1ZscCAAAObmZ8AACA0RN8AACA0Zs6+FTVE6qqq+rEYfvUqnr3gjFnVdWThvULqurKqrq0qj5ZVa+vqjtOWwcAAMBSZjHj89Qkfzv8eaCe1t0PSPKAJDcl+csZ1AEAALCoqYJPVR2e5OFJnp3kKbd0/+7+dpLfSHKPqnrgNLUAAAAsZdoZn8cneW93fyrJV6rqlFt6gO7ek+SSJCcu7KuqbVU1V1Vze27cPWWpAADAejVt8HlqknOG9XOG7V5i7FLtSVKL7tC9s7u3dPeWDRs3Lb9KAABgXVv27/GpqiOSPCLJSVXVSTZkPtycneROC4YfkeT6JY6zIclJSa5Ybi0AAAD7Ms2Mz5OS/El3H9/dm7v77kk+k/mQc7eq+pEkqarjkzwwycULD1BVt0nyu0k+292XTlELAADAkpY945P529p+b0HbOzL/koOnJ/njqjosyXeS/FJ3Tz6k86dVdVOSQ5N8IPPPCgEAAKyIZQef7j5tkbbXTWw+ZIn9Tl3uOQEAAJZjFr/HBwAA4KA2za1ut6qTjt2UuR1bV7sMAABgDTLjAwAAjJ7gAwAAjJ7gAwAAjJ7gAwAAjJ7gAwAAjJ7gAwAAjJ7gAwAAjJ7gAwAAjJ7gAwAAjJ7gAwAAjJ7gAwAAjJ7gAwAAjJ7gAwAAjN4hq13Agbrs2t3ZvP281S7jZnbt2LraJQAAAAfAjA8AADB6gg8AADB6UwWfqrprVZ1TVddU1UVV9Z6q+uGh7wVV9a2q2jQx/tSq2l1VH6uqK6vqb6rqcdP+EAAAAPuy7OBTVZXknUku6O57d/cpSV6S5OhhyFOTXJjkiQt2/XB3P6i775Pk+UleX1U/tdw6AAAA9meaGZ/Tknynu9+4t6G7L+nuD1fVvZMcnuRlmQ9Ai+rui5P8dpLnTVEHAADAPk0TfO6f5KIl+p6S5JwkH05yn6o6eolxSfLRJCdOUQcAAMA+rdTLDZ6a5Jzu/uck70jyc/sYW0t2VG2rqrmqmttz4+5Z1wgAAKwT0wSfy5OcsrCxqk5KckKS86tqV+Znf5a83S3Jg5JcsVhHd+/s7i3dvWXDxk2LDQEAANivaYLPB5McWlXb9jZU1QOSvC7J6d29eVjuluRuVXX8wgMM4/+fJGdOUQcAAMA+HbLcHbu7q+pnk7ymqn4zybeS7EpyapJfWTD8nZmf+flIkp+oqo8l2ZjkuiTP7+6/Xm4dAAAA+7Ps4JMk3f35JD9/AON+bWLTPWsAAMCtaqVebgAAAHDQmGrG59Z00rGbMrdj62qXAQAArEFmfAAAgNETfAAAgNETfAAAgNETfAAAgNETfAAAgNETfAAAgNETfAAAgNETfAAAgNETfAAAgNETfAAAgNETfAAAgNETfAAAgNETfAAAgNE7ZLULOFCXXbs7m7eft9plLGrXjq2rXQIAALAPZnwAAIDRE3wAAIDRmzr4VNWeqrp4Ytk+tO+qqqMmxp1aVe8e1p9VVV8exn+yql44bR0AAABLmcUzPt/s7pOXsd+53f28qjoyyZVV9fbu/uwM6gEAALiZVb/Vrbu/kuTqJMesdi0AAMA4zSL43G7BrW5PviU7V9U9khyW5NIZ1AIAAPB9VvJWt95P25Or6l8mOTHJ87r7WwsHV9W2JNuSZMMd7jyDUgEAgPVoJW91+0qSO01sH5Hk+ontc7v7AUkemmRHVd114QG6e2d3b+nuLRs2blrBUgEAgDFbyeBzQZJ/myRVtSHJ05N8aOGg7p5L8idJfnUFawEAANaxlXjGZ8fQ/v8m+aGquiTJxzL/AoO3LHGM30vyi1V1+xnUAwAAcDNTP+PT3RuWaN+d5BeW6DsryVkT259P8n23ugEAAMzCqr/OGgAAYKUJPgAAwOjN4nXWt4qTjt2UuR1bV7sMAABgDTLjAwAAjJ7gAwAAjJ7gAwAAjJ7gAwAAjJ7gAwAAjJ7gAwAAjJ7gAwAAjJ7gAwAAjJ7gAwAAjJ7gAwAAjJ7gAwAAjJ7gAwAAjN4hq13Agbrs2t3ZvP281S5jNHbt2LraJQAAwK3GjA8AADB6gg8AADB6gg8AADB6Uwefqjq6qt5aVZ+uqouq6n9V1c9W1alVtbuqLq6qS6vqA1V1l2GfZ1XVl4e+T1TVv5/+RwEAAFjcVMGnqirJ/0jyN919r+4+JclTkhw3DPlwd5/c3Q9IcmGS507sfm53n5zk1CS/U1VHT1MLAADAUqad8XlEkm939xv3NnT3P3T3f50cNASk2yf5x4UH6O7rklyT5PgpawEAAFjUtK+zvl+Sj+6j/yeq6uIkRyb5RpLfWjigqu6V5F5Jrl6kb1uSbUmy4Q53nrJUAABgvZrpyw2q6syquqSqLhya9t7qdvckf5zkjInhTx5C0Z8l+eXu/urC43X3zu7e0t1bNmzcNMtSAQCAdWTaGZ/Lk/ybvRvd/dyqOirJ3CJj35XkHRPb53b386Y8PwAAwH5NO+PzwSSHVdWvTLRtXGLswzP/LA8AAMCtaqoZn+7uqnpCkldX1W8k+XLmn+X5zWHI3md8KsnuJL80zfkAAACWY9pb3dLdX8j8K6wXs+iDOd19VpKzpj03AADAgZjpyw0AAAAORlPP+NxaTjp2U+Z2bF3tMgAAgDXIjA8AADB6gg8AADB6gg8AADB6gg8AADB6gg8AADB6gg8AADB6gg8AADB6gg8AADB6gg8AADB6gg8AADB6gg8AADB6gg8AADB6h6x2AQfqsmt3Z/P281a7DAAAIMmuHVtXu4RbxIwPAAAweoIPAAAweoIPAAAwelMFn6raXFUfX9B2elW9uKoeUlUfqaqLq+qKqjp96H9WVX25qj5WVVdV1fuq6qHT1AEAALAvK/lyg7OT/Hx3X1JVG5LcZ6Lv3O5+XpJU1WlJ/qKqTuvuK1awHgAAYJ1ayVvd7pLkC0nS3Xu6+xOLDeruDyXZmWTbCtYCAACsYysZfF6d5MqqemdV/XJVHbaPsR9NcuLCxqraVlVzVTW358bdK1YoAAAwbtMGn16qvbt/O8mWJO9P8gtJ3ruP49QSB9nZ3Vu6e8uGjZumqxQAAFi3pg0+X0lypwVtRyS5Pkm6+5rufkOSn0rywKo6conjPCiJ53sAAIAVMVXw6e6vJ/lCVT0iSarqiCQ/k+Rvq2prVe2dyTkhyZ4kX1t4jKr6ycw/3/PfpqkFAABgKbN4q9szkpxZVX8wbL+8u6+pqlcmeXVV3Zjku0me1t17hiz05Kp6eJKNST6T5N94oxsAALBSpg4+w9vaTluk/SlLjD8ryVnTnhcAAOBAreRb3QAAAA4KK/kLTGfqpGM3ZW7H1tUuAwAAWIPM+AAAAKMn+AAAAKMn+AAAAKMn+AAAAKMn+AAAAKMn+AAAAKNX3b3aNRyQqvqnJFeudh0cdI5Kcv1qF8FBx3XBYlwXLMZ1wWJcF2vb8d1954WNa+b3+CS5sru3rHYRHFyqas51wUKuCxbjumAxrgsW47oYJ7e6AQAAoyf4AAAAo7eWgs/O1S6Ag5LrgsW4LliM64LFuC5YjOtihNbMyw0AAACWay3N+AAAACzLmgg+VfUzVXVlVV1dVdtXux5mo6reVFXXVdXHJ9qOqKrzq+qq4c87De1VVa8broFLq+rBE/s8cxh/VVU9c6L9lKq6bNjndVVV+zoHq6+q7l5VH6qqT1TV5VX1q0O762Idq6rDqurvq+qS4bp4+dB+z6r6yPBZnltVtx3aDx22rx76N08c6yVD+5VV9eiJ9kW/Z5Y6BwePqtpQVR+rqncP266Lda6qdg1/z19cVXNDm+8Rku4+qJckG5Jck+ReSW6b5JIk913tuiwz+Wz/ZZIHJ/n4RNsZSbYP69uT/N6w/tgkf5WkkjwkyUeG9iOSfHr4807D+p2Gvr8fxtaw72P2dQ7L6i9Jjkny4GH99kk+leS+rov1vQyf1eHD+m2SfGT4DP88yVOG9jcm+ZVh/T8keeOw/pQk5w7r9x2+Qw5Ncs/hu2XDvr5nljqH5eBZkvxakrcmefe+PjPXxfpZkuxKctSCNt8jljUx4/NjSa7u7k9397eTnJPk8atcEzPQ3X+T5KsLmh+f5Oxh/ewkT5hof3PP+99J7lhVxyR5dJLzu/ur3f2PSc5P8jND3x26+3/3/N9Ab15wrMXOwSrr7i9090eH9X9KckWSY+O6WNeGz/frw+ZthqWTPCLJ24f2hdfF3s/y7Ul+avgX2ccnOae7b+ruzyS5OvPfMYt+zwz7LHUODgJVdVySrUn++7C9r8/MdbG++R5hTQSfY5N8dmL7c0Mb43R0d39hWP9ikqOH9aWug321f26R9n2dg4PIcBvKgzL/r/uui3VuuJ3p4iTXZf5/QK5J8rXu/u4wZPKz/N7nP/TvTnJkbvn1cuQ+zsHB4TVJfiPJPw/b+/rMXBfrRyd5f1VdVFXbhjbfI+SQ1S4AlriPBRcAAAKBSURBVNLdXVUr+trBW+Mc3HJVdXiSdyR5QXffMNw+ncR1sV51954kJ1fVHZO8M8mJq1wSq6yqHpfkuu6+qKpOXe16OKg8vLuvraq7JDm/qj452el7ZP1aCzM+1ya5+8T2cUMb4/SlYRo5w5/XDe1LXQf7aj9ukfZ9nYODQFXdJvOh50+7+y+GZtcFSZLu/lqSDyX58czfkrL3H/AmP8vvff5D/6YkX8ktv16+so9zsPoeluRfV9WuzN+G9ogkr43rYt3r7muHP6/L/D+U/Fh8j5C1EXwuTHLC8AaV22b+gcR3rXJNrJx3Jdn75pRnJvnLifZnDG9feUiS3cN08vuSPKqq7jS8PeVRSd439N1QVQ8Z7sd+xoJjLXYOVtnwWf1Rkiu6+w8mulwX61hV3XmY6UlV3S7JT2f++a8PJXnSMGzhdbH3s3xSkg8O9+K/K8lTav7tXvdMckLmH1Je9Htm2Gepc7DKuvsl3X1cd2/O/Gf2we5+WlwX61pV/WBV3X7veub//v94fI+QHPxvdZv/+yWPzfzbna5J8tLVrscys8/1z5J8Icl3Mn+P7LMzf+/0Xye5KskHkhwxjK0kZw7XwGVJtkwc599l/mHUq5P84kT7lsz/ZXdNktfn//7C3kXPYVn9JcnDM39v9qVJLh6Wx7ou1veS5AFJPjZcFx9P8p+G9ntl/n9Qr07ytiSHDu2HDdtXD/33mjjWS4fP/soMb2Ia2hf9nlnqHJaDa0lyav7vW91cF+t4GT6bS4bl8r2fm+8RS3d/74MCAAAYrbVwqxsAAMBUBB8AAGD0BB8AAGD0BB8AAGD0BB8AAGD0BB8AAGD0BB8AAGD0BB8AAGD0/n9ANLKbaVvu1QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4tdrALSHpYu5"
      },
      "source": [
        "ks['country'] = fct_lump(ks['country'], n= 9)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "ngbxiS3MpYu5",
        "outputId": "2f56110c-7194-4677-aa09-ebf40b4bb97d"
      },
      "source": [
        "ks['country'].value_counts().plot(kind='barh', figsize=(14,6), title='Countries')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa19b9e1240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RY4PO-1upYu6",
        "outputId": "8f3fa969-3eb1-40c1-ffa0-684074bf5371"
      },
      "source": [
        "ks['country'].value_counts()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "US       550192\n",
              "GB        61181\n",
              "CA        26748\n",
              "Other     24959\n",
              "AU        14075\n",
              "DE         6855\n",
              "NL         5127\n",
              "FR         4849\n",
              "IT         4628\n",
              "N,0\"       3797\n",
              "Name: country, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "68mqAuh8pYu6",
        "outputId": "882bc0eb-f2a2-4e8a-f37d-0448d6bb4796"
      },
      "source": [
        "ks['state'].isnull().sum()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "333I62yipYu7",
        "outputId": "f0d24172-814a-498c-809b-eb963c83ecce"
      },
      "source": [
        "ks['state'].isna().sum()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v4fWPgyJpYu7"
      },
      "source": [
        "def state (series):\n",
        "    if series == 'successful':\n",
        "        return 'successful'\n",
        "    else:\n",
        "        return 'failed'\n",
        "\n",
        "ks['state_lump'] = ks['state'].apply(state)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "YAmGNMBJpYu8",
        "outputId": "13c08ce8-fefb-4b01-81e1-6d55fb39f8f6"
      },
      "source": [
        "ks['state_lump'].value_counts().plot(kind='barh', figsize=(14,6), title='state of the projects')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa19b9e1470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "JGmJyBYepYu8",
        "outputId": "dc861083-1012-45e1-f2f1-5f78bb5f4cf1"
      },
      "source": [
        "ks.head()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>name</th>\n",
              "      <th>category</th>\n",
              "      <th>main_category</th>\n",
              "      <th>currency</th>\n",
              "      <th>deadline</th>\n",
              "      <th>goal</th>\n",
              "      <th>launched</th>\n",
              "      <th>pledged</th>\n",
              "      <th>state</th>\n",
              "      <th>backers</th>\n",
              "      <th>country</th>\n",
              "      <th>usd.pledged</th>\n",
              "      <th>state_lump</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000002330</td>\n",
              "      <td>The Songs of Adelaide &amp; Abullah</td>\n",
              "      <td>Poetry</td>\n",
              "      <td>Publishing</td>\n",
              "      <td>GBP</td>\n",
              "      <td>2015-10-09</td>\n",
              "      <td>1000</td>\n",
              "      <td>2015-08-11 12:12:28</td>\n",
              "      <td>0</td>\n",
              "      <td>failed</td>\n",
              "      <td>0</td>\n",
              "      <td>GB</td>\n",
              "      <td>0</td>\n",
              "      <td>failed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000003930</td>\n",
              "      <td>Greeting From Earth: ZGAC Arts Capsule For ET</td>\n",
              "      <td>Narrative Film</td>\n",
              "      <td>Film &amp; Video</td>\n",
              "      <td>USD</td>\n",
              "      <td>2017-11-01</td>\n",
              "      <td>30000</td>\n",
              "      <td>2017-09-02 04:43:57</td>\n",
              "      <td>2421</td>\n",
              "      <td>failed</td>\n",
              "      <td>15</td>\n",
              "      <td>US</td>\n",
              "      <td>100</td>\n",
              "      <td>failed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000004038</td>\n",
              "      <td>Where is Hank?</td>\n",
              "      <td>Narrative Film</td>\n",
              "      <td>Film &amp; Video</td>\n",
              "      <td>USD</td>\n",
              "      <td>2013-02-26</td>\n",
              "      <td>45000</td>\n",
              "      <td>2013-01-12 00:20:50</td>\n",
              "      <td>220</td>\n",
              "      <td>failed</td>\n",
              "      <td>3</td>\n",
              "      <td>US</td>\n",
              "      <td>220</td>\n",
              "      <td>failed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000007540</td>\n",
              "      <td>ToshiCapital Rekordz Needs Help to Complete Album</td>\n",
              "      <td>Music</td>\n",
              "      <td>Music</td>\n",
              "      <td>USD</td>\n",
              "      <td>2012-04-16</td>\n",
              "      <td>5000</td>\n",
              "      <td>2012-03-17 03:24:11</td>\n",
              "      <td>1</td>\n",
              "      <td>failed</td>\n",
              "      <td>1</td>\n",
              "      <td>US</td>\n",
              "      <td>1</td>\n",
              "      <td>failed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000011046</td>\n",
              "      <td>Community Film Project: The Art of Neighborhoo...</td>\n",
              "      <td>Film &amp; Video</td>\n",
              "      <td>Film &amp; Video</td>\n",
              "      <td>USD</td>\n",
              "      <td>2015-08-29</td>\n",
              "      <td>19500</td>\n",
              "      <td>2015-07-04 08:35:03</td>\n",
              "      <td>1283</td>\n",
              "      <td>canceled</td>\n",
              "      <td>14</td>\n",
              "      <td>US</td>\n",
              "      <td>1283</td>\n",
              "      <td>failed</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ID  ... state_lump\n",
              "0  1000002330  ...     failed\n",
              "1  1000003930  ...     failed\n",
              "2  1000004038  ...     failed\n",
              "3  1000007540  ...     failed\n",
              "4  1000011046  ...     failed\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KqkrzLQQpYu9"
      },
      "source": [
        "from datetime import datetime\n",
        "from datetime import timedelta"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CMGStd5upYu9"
      },
      "source": [
        "ks['launched'] = pd.to_datetime(ks['launched'], format='%Y-%m-%d', errors='coerce' )\n",
        "ks['deadline'] = pd.to_datetime(ks['deadline'], format='%Y-%m-%d', errors='coerce' )"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "ZgptoyumpYu9",
        "outputId": "1668e099-bb43-4d8f-e4f1-83d18a67693f"
      },
      "source": [
        "ks['launched_year']=ks['launched'].dt.year\n",
        "ks['launched_year'].dropna(inplace=True)\n",
        "ks['launched_year'] = ks['launched_year'].astype(int)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-33-1324f48f6dea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'launched_year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'launched'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'launched_year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'launched_year'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'launched_year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   5546\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5547\u001b[0m             \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5548\u001b[0;31m             \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5549\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    602\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m     ) -> \"BlockManager\":\n\u001b[0;32m--> 604\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    605\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    606\u001b[0m     def convert(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m    407\u001b[0m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 409\u001b[0;31m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    410\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    411\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    593\u001b[0m             \u001b[0mvals1d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 595\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals1d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    596\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m                 \u001b[0;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m    966\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 968\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot convert non-finite values (NA or inf) to integer\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    969\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    970\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Cannot convert non-finite values (NA or inf) to integer"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kf8PLFghpYu-"
      },
      "source": [
        "ks['launched_month']=ks['launched'].dt.month"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xyOqD6zJpYu-"
      },
      "source": [
        "ks['launched_day']=ks['launched'].dt.day"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "_3_LwpLXpYu-",
        "outputId": "6861faaf-a2e5-4429-c453-e3f7fc63d9cd"
      },
      "source": [
        "ks['launched_year'].value_counts().plot(kind='barh', figsize=(14,6), title='project launch years')"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa19b9c4048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "G9yUzNM7pYvE",
        "outputId": "36fd3416-0abd-4be7-88ab-d485ba9f90ab"
      },
      "source": [
        "# visualize the date between years 2009-2017\n",
        "a = ks[(ks['launched_year'] < 2018) & (ks['launched_year'] > 2007)]\n",
        "a.head()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>name</th>\n",
              "      <th>category</th>\n",
              "      <th>main_category</th>\n",
              "      <th>currency</th>\n",
              "      <th>deadline</th>\n",
              "      <th>goal</th>\n",
              "      <th>launched</th>\n",
              "      <th>pledged</th>\n",
              "      <th>state</th>\n",
              "      <th>backers</th>\n",
              "      <th>country</th>\n",
              "      <th>usd.pledged</th>\n",
              "      <th>state_lump</th>\n",
              "      <th>launched_year</th>\n",
              "      <th>launched_month</th>\n",
              "      <th>launched_day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000002330</td>\n",
              "      <td>The Songs of Adelaide &amp; Abullah</td>\n",
              "      <td>Poetry</td>\n",
              "      <td>Publishing</td>\n",
              "      <td>GBP</td>\n",
              "      <td>2015-10-09</td>\n",
              "      <td>1000</td>\n",
              "      <td>2015-08-11 12:12:28</td>\n",
              "      <td>0</td>\n",
              "      <td>failed</td>\n",
              "      <td>0</td>\n",
              "      <td>GB</td>\n",
              "      <td>0</td>\n",
              "      <td>failed</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000003930</td>\n",
              "      <td>Greeting From Earth: ZGAC Arts Capsule For ET</td>\n",
              "      <td>Narrative Film</td>\n",
              "      <td>Film &amp; Video</td>\n",
              "      <td>USD</td>\n",
              "      <td>2017-11-01</td>\n",
              "      <td>30000</td>\n",
              "      <td>2017-09-02 04:43:57</td>\n",
              "      <td>2421</td>\n",
              "      <td>failed</td>\n",
              "      <td>15</td>\n",
              "      <td>US</td>\n",
              "      <td>100</td>\n",
              "      <td>failed</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000004038</td>\n",
              "      <td>Where is Hank?</td>\n",
              "      <td>Narrative Film</td>\n",
              "      <td>Film &amp; Video</td>\n",
              "      <td>USD</td>\n",
              "      <td>2013-02-26</td>\n",
              "      <td>45000</td>\n",
              "      <td>2013-01-12 00:20:50</td>\n",
              "      <td>220</td>\n",
              "      <td>failed</td>\n",
              "      <td>3</td>\n",
              "      <td>US</td>\n",
              "      <td>220</td>\n",
              "      <td>failed</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000007540</td>\n",
              "      <td>ToshiCapital Rekordz Needs Help to Complete Album</td>\n",
              "      <td>Music</td>\n",
              "      <td>Music</td>\n",
              "      <td>USD</td>\n",
              "      <td>2012-04-16</td>\n",
              "      <td>5000</td>\n",
              "      <td>2012-03-17 03:24:11</td>\n",
              "      <td>1</td>\n",
              "      <td>failed</td>\n",
              "      <td>1</td>\n",
              "      <td>US</td>\n",
              "      <td>1</td>\n",
              "      <td>failed</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000011046</td>\n",
              "      <td>Community Film Project: The Art of Neighborhoo...</td>\n",
              "      <td>Film &amp; Video</td>\n",
              "      <td>Film &amp; Video</td>\n",
              "      <td>USD</td>\n",
              "      <td>2015-08-29</td>\n",
              "      <td>19500</td>\n",
              "      <td>2015-07-04 08:35:03</td>\n",
              "      <td>1283</td>\n",
              "      <td>canceled</td>\n",
              "      <td>14</td>\n",
              "      <td>US</td>\n",
              "      <td>1283</td>\n",
              "      <td>failed</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ID  ... launched_day\n",
              "0  1000002330  ...         11.0\n",
              "1  1000003930  ...          2.0\n",
              "2  1000004038  ...         12.0\n",
              "3  1000007540  ...         17.0\n",
              "4  1000011046  ...          4.0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "SDg7WKJtpYvF",
        "outputId": "1ce3c4b5-3ba8-43c2-f3d1-ee0ca9f6126e"
      },
      "source": [
        "a['launched_year'].value_counts().plot(kind='barh', figsize=(14,6), title='project launch years')"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa19b6adbe0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C0H2EMe-pYvF",
        "outputId": "520685e9-ce23-4af9-d349-a50022d2b8ab"
      },
      "source": [
        "ks_dummy = pd.get_dummies(ks, columns = ['main_category','state_lump', 'currency', 'country' ], drop_first = True)\n",
        "\n",
        "print(list(ks_dummy.columns))\n"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['ID', 'name', 'category', 'deadline', 'goal', 'launched', 'pledged', 'state', 'backers', 'usd.pledged', 'launched_year', 'launched_month', 'launched_day', 'main_category_Art', 'main_category_Design', 'main_category_Fashion', 'main_category_Film & Video', 'main_category_Food', 'main_category_Games', 'main_category_Music', 'main_category_Photography', 'main_category_Publishing', 'main_category_Technology', 'main_category_Theater', 'state_lump_successful', 'currency_AUD', 'currency_CAD', 'currency_CHF', 'currency_DKK', 'currency_EUR', 'currency_GBP', 'currency_MXN', 'currency_NOK', 'currency_NZD', 'currency_SEK', 'currency_USD', 'country_AU', 'country_CA', 'country_DE', 'country_FR', 'country_GB', 'country_IT', 'country_N,0\"', 'country_NL', 'country_US']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "dVlbCCBVpYvG",
        "outputId": "a919cee7-6bc6-404d-9ccc-2062141b7161"
      },
      "source": [
        "ks_dummy.head()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>name</th>\n",
              "      <th>category</th>\n",
              "      <th>deadline</th>\n",
              "      <th>goal</th>\n",
              "      <th>launched</th>\n",
              "      <th>pledged</th>\n",
              "      <th>state</th>\n",
              "      <th>backers</th>\n",
              "      <th>usd.pledged</th>\n",
              "      <th>launched_year</th>\n",
              "      <th>launched_month</th>\n",
              "      <th>launched_day</th>\n",
              "      <th>main_category_Art</th>\n",
              "      <th>main_category_Design</th>\n",
              "      <th>main_category_Fashion</th>\n",
              "      <th>main_category_Film &amp; Video</th>\n",
              "      <th>main_category_Food</th>\n",
              "      <th>main_category_Games</th>\n",
              "      <th>main_category_Music</th>\n",
              "      <th>main_category_Photography</th>\n",
              "      <th>main_category_Publishing</th>\n",
              "      <th>main_category_Technology</th>\n",
              "      <th>main_category_Theater</th>\n",
              "      <th>state_lump_successful</th>\n",
              "      <th>currency_AUD</th>\n",
              "      <th>currency_CAD</th>\n",
              "      <th>currency_CHF</th>\n",
              "      <th>currency_DKK</th>\n",
              "      <th>currency_EUR</th>\n",
              "      <th>currency_GBP</th>\n",
              "      <th>currency_MXN</th>\n",
              "      <th>currency_NOK</th>\n",
              "      <th>currency_NZD</th>\n",
              "      <th>currency_SEK</th>\n",
              "      <th>currency_USD</th>\n",
              "      <th>country_AU</th>\n",
              "      <th>country_CA</th>\n",
              "      <th>country_DE</th>\n",
              "      <th>country_FR</th>\n",
              "      <th>country_GB</th>\n",
              "      <th>country_IT</th>\n",
              "      <th>country_N,0\"</th>\n",
              "      <th>country_NL</th>\n",
              "      <th>country_US</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000002330</td>\n",
              "      <td>The Songs of Adelaide &amp; Abullah</td>\n",
              "      <td>Poetry</td>\n",
              "      <td>2015-10-09</td>\n",
              "      <td>1000</td>\n",
              "      <td>2015-08-11 12:12:28</td>\n",
              "      <td>0</td>\n",
              "      <td>failed</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000003930</td>\n",
              "      <td>Greeting From Earth: ZGAC Arts Capsule For ET</td>\n",
              "      <td>Narrative Film</td>\n",
              "      <td>2017-11-01</td>\n",
              "      <td>30000</td>\n",
              "      <td>2017-09-02 04:43:57</td>\n",
              "      <td>2421</td>\n",
              "      <td>failed</td>\n",
              "      <td>15</td>\n",
              "      <td>100</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000004038</td>\n",
              "      <td>Where is Hank?</td>\n",
              "      <td>Narrative Film</td>\n",
              "      <td>2013-02-26</td>\n",
              "      <td>45000</td>\n",
              "      <td>2013-01-12 00:20:50</td>\n",
              "      <td>220</td>\n",
              "      <td>failed</td>\n",
              "      <td>3</td>\n",
              "      <td>220</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000007540</td>\n",
              "      <td>ToshiCapital Rekordz Needs Help to Complete Album</td>\n",
              "      <td>Music</td>\n",
              "      <td>2012-04-16</td>\n",
              "      <td>5000</td>\n",
              "      <td>2012-03-17 03:24:11</td>\n",
              "      <td>1</td>\n",
              "      <td>failed</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000011046</td>\n",
              "      <td>Community Film Project: The Art of Neighborhoo...</td>\n",
              "      <td>Film &amp; Video</td>\n",
              "      <td>2015-08-29</td>\n",
              "      <td>19500</td>\n",
              "      <td>2015-07-04 08:35:03</td>\n",
              "      <td>1283</td>\n",
              "      <td>canceled</td>\n",
              "      <td>14</td>\n",
              "      <td>1283</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ID  ... country_US\n",
              "0  1000002330  ...          0\n",
              "1  1000003930  ...          1\n",
              "2  1000004038  ...          1\n",
              "3  1000007540  ...          1\n",
              "4  1000011046  ...          1\n",
              "\n",
              "[5 rows x 45 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dEu3ceTPpYvH",
        "outputId": "6b57012d-c37f-4878-c405-59db8a0b2f5d"
      },
      "source": [
        "ks_dummy['deadline']=pd.to_datetime(ks_dummy['deadline'], format=\"%Y/%m/%d\",  errors='coerce').dt.date\n",
        "ks_dummy['launched']=pd.to_datetime(ks_dummy['launched'], format=\"%Y/%m/%d\").dt.date\n",
        "\n",
        "\n",
        "ks_dummy['longevity'] = (ks_dummy['deadline'] - ks_dummy['launched']).dt.days\n",
        "\n",
        "ks_dummy.longevity.head()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    59.0\n",
              "1    60.0\n",
              "2    45.0\n",
              "3    30.0\n",
              "4    56.0\n",
              "Name: longevity, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y5N7vfw_pYvH"
      },
      "source": [
        "# How many days from launch day to deadline has passed for each project. "
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1KOSHwntpYvH"
      },
      "source": [
        "ks_mean = ks[['main_category','pledged']]"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mFJgLkGMpYvI"
      },
      "source": [
        "ks_mean1 = pd.to_numeric(ks_mean['pledged'] , errors='coerce').groupby(ks_mean['main_category']).mean()"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZhOL2rcPpYvI",
        "outputId": "e997bcbb-873c-404e-e399-9a809ce5062d"
      },
      "source": [
        "ks_mean1 = pd.DataFrame(ks_mean1)\n",
        "sorted_pledge = ks_mean1.sort_values('pledged', ascending=False)\n",
        "print(sorted_pledge)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                    pledged\n",
            "main_category              \n",
            "Design         25788.671833\n",
            "Technology     21848.222988\n",
            "Games          21351.519171\n",
            "Fashion         6327.620362\n",
            "Film & Video    6269.469818\n",
            "Food            5217.865902\n",
            "Other           4200.032061\n",
            "Theater         4022.504375\n",
            "Music           3919.288843\n",
            "Publishing      3477.816587\n",
            "Photography     3462.573504\n",
            "Art             3420.555728\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jkhsMQ4dpYvJ"
      },
      "source": [
        "Design, Technology and Games have the highest mean of pledged money. The trio is the leader of categories by far!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d-gGvwHRpYvK",
        "outputId": "a3acec1f-a10c-4b8f-cc88-99b894802e95"
      },
      "source": [
        "ks_dummy['category'] = fct_lump(ks_dummy['category'], n= 11)\n",
        "ks_days = ks_dummy[['category','longevity']]\n",
        "ks_days = pd.to_numeric(ks_days['longevity'] , errors='coerce').groupby(ks_days['category']).mean()\n",
        "ks_days = pd.DataFrame(ks_days)\n",
        "sorted_days = ks_days.sort_values('longevity', ascending = False)\n",
        "print(sorted_days)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                longevity\n",
            "category                 \n",
            "Film & Video    39.384416\n",
            "Documentary     37.135933\n",
            "Music           36.267666\n",
            "Product Design  34.971752\n",
            "Other           34.470363\n",
            "Nonfiction      34.456558\n",
            "Fiction         34.090575\n",
            "Video Games     34.036320\n",
            "Food            33.980114\n",
            "Fashion         33.432945\n",
            "Shorts          33.360429\n",
            "Tabletop Games  31.194190\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bBiJCHz4pYvL"
      },
      "source": [
        "#Film & Video projects has longest campaign days"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zb28Cqw4pYvL",
        "outputId": "c37ee49e-6b8f-4926-bf84-9d3b558ae93b"
      },
      "source": [
        "ks_mean2 = pd.to_numeric(ks['backers'] , errors='coerce').groupby(ks['main_category']).mean()\n",
        "ks_mean2 = pd.DataFrame(ks_mean2)\n",
        "sorted_backers = ks_mean2.sort_values('backers', ascending = False)\n",
        "print(sorted_backers)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                  backers\n",
            "main_category            \n",
            "Games          322.834224\n",
            "Design         239.612552\n",
            "Technology     165.021999\n",
            "Other           73.767558\n",
            "Film & Video    66.234488\n",
            "Fashion         61.812661\n",
            "Publishing      54.273865\n",
            "Food            54.262380\n",
            "Music           51.784795\n",
            "Theater         47.227210\n",
            "Art             40.934986\n",
            "Photography     38.603581\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iq4kTb45pYvM"
      },
      "source": [
        "Design, Technology and Games have the highest mean of number of backers. The trio is leader for pledged mean as well, so we can understand the strong correlation of number of backers and pledged amount. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZU8o7IArpYvM"
      },
      "source": [
        "import csv"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q3WdS-nvpYvM"
      },
      "source": [
        "\n",
        "filename = 'ks_dummy_coded.csv'\n",
        "with open (filename, 'w') as csvfile:\n",
        "    csvwriter = csv.writer(csvfile)\n",
        "    csvwriter.writerow(list(ks_dummy.columns))\n",
        "    csvwriter.writerows(ks_dummy)\n",
        "    # I can not believe writing data to csv in python is so much painful!!!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ze8ScDfIpYvN"
      },
      "source": [
        "# Statistical Tests"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ZBHefH4pYvN"
      },
      "source": [
        "Hypothesis: According to Kickstarter website stats, around 60% of the projects have failed. We want to test if this statistic is represented in our data using Goodness of Fit Chi-Squares test. (Source: https://www.kickstarter.com/help/stats)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x4XCjJggpYvN"
      },
      "source": [
        "import scipy, scipy.stats"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h2FTBHFypYvO",
        "outputId": "73ff89e6-fee3-4784-8022-f82254a94974"
      },
      "source": [
        "ks.state_lump.value_counts()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "failed        455374\n",
              "successful    247037\n",
              "Name: state_lump, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ThEAD3F8pYvO"
      },
      "source": [
        "observed_values = scipy.array([455374, 247037])\n",
        "expected_values = scipy.array([421446, 280965])"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2wS3kj4bpYvO",
        "outputId": "cdf2dd27-99f5-4df8-9f78-c585b11dced3"
      },
      "source": [
        "scipy.stats.chisquare(observed_values, f_exp=expected_values)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Power_divergenceResult(statistic=6828.3168294956, pvalue=0.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HjPCO7PLpYvP"
      },
      "source": [
        "According to Goodness of Fit Chi-Squares test, Our data is significantly different than Kickstarter source data. Most likely the reason for this difference is we gathered all the values other than 'successfull' as 'failed ' under state column. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W2neuVIWpYvP"
      },
      "source": [
        "Independent Chi-Square\n",
        "Hypothesis: Main category and project state are independent. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "amCiXtAIpYvP",
        "outputId": "ad806b39-cc27-440c-89bc-fc3fc0d76f09"
      },
      "source": [
        "category_crosstab = pd.crosstab(ks['main_category'], ks['state_lump'])\n",
        "category_crosstab\n"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>state_lump</th>\n",
              "      <th>failed</th>\n",
              "      <th>successful</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>main_category</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>31536</td>\n",
              "      <td>20447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Art</th>\n",
              "      <td>30964</td>\n",
              "      <td>21164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Design</th>\n",
              "      <td>35433</td>\n",
              "      <td>18509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fashion</th>\n",
              "      <td>31311</td>\n",
              "      <td>9903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Film &amp; Video</th>\n",
              "      <td>76237</td>\n",
              "      <td>45027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Food</th>\n",
              "      <td>34490</td>\n",
              "      <td>11341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Games</th>\n",
              "      <td>41336</td>\n",
              "      <td>21903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Music</th>\n",
              "      <td>52702</td>\n",
              "      <td>45960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Photography</th>\n",
              "      <td>14251</td>\n",
              "      <td>6208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Publishing</th>\n",
              "      <td>51552</td>\n",
              "      <td>22555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Technology</th>\n",
              "      <td>47201</td>\n",
              "      <td>11496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Theater</th>\n",
              "      <td>8361</td>\n",
              "      <td>12524</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "state_lump     failed  successful\n",
              "main_category                    \n",
              "Other           31536       20447\n",
              "Art             30964       21164\n",
              "Design          35433       18509\n",
              "Fashion         31311        9903\n",
              "Film & Video    76237       45027\n",
              "Food            34490       11341\n",
              "Games           41336       21903\n",
              "Music           52702       45960\n",
              "Photography     14251        6208\n",
              "Publishing      51552       22555\n",
              "Technology      47201       11496\n",
              "Theater          8361       12524"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sg2BtnegpYvQ"
      },
      "source": [
        "Crosstab for Main category and project state. Table shows frequencies for main category. \n",
        "Assumption of 5 cases per expected cell is passed easily. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BYA2DqcMpYvQ",
        "outputId": "8f08a151-0ddf-46b3-88a7-80b1132ca639"
      },
      "source": [
        "stats.chi2_contingency(category_crosstab)"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(24186.5769647064, 0.0, 11, array([[33700.64910999, 18282.35089001],\n",
              "        [33794.65280584, 18333.34719416],\n",
              "        [34970.67145589, 18971.32854411],\n",
              "        [26719.09186502, 14494.90813498],\n",
              "        [78615.61498325, 42648.38501675],\n",
              "        [29712.2992009 , 16118.7007991 ],\n",
              "        [40997.92911273, 22241.07088727],\n",
              "        [63962.70785623, 34699.29214377],\n",
              "        [13263.59733262,  7195.40266738],\n",
              "        [48043.66819141, 26063.33180859],\n",
              "        [38053.34437815, 20643.65562185],\n",
              "        [13539.77370799,  7345.22629201]]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yitrEku9pYvR"
      },
      "source": [
        "our p value is significant which means independence is rejected, there is significant correlations between main category and project state. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "YHXLlVe2pYvR",
        "outputId": "4f79dd70-0af3-45f9-a918-14281e8d4c34"
      },
      "source": [
        "country_crosstab = pd.crosstab(ks['country'], ks['state_lump'])\n",
        "country_crosstab"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>state_lump</th>\n",
              "      <th>failed</th>\n",
              "      <th>successful</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>19749</td>\n",
              "      <td>5210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AU</th>\n",
              "      <td>10615</td>\n",
              "      <td>3460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CA</th>\n",
              "      <td>19547</td>\n",
              "      <td>7201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DE</th>\n",
              "      <td>5388</td>\n",
              "      <td>1467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FR</th>\n",
              "      <td>3429</td>\n",
              "      <td>1420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GB</th>\n",
              "      <td>39774</td>\n",
              "      <td>21407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IT</th>\n",
              "      <td>3957</td>\n",
              "      <td>671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>N,0\"</th>\n",
              "      <td>3692</td>\n",
              "      <td>105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NL</th>\n",
              "      <td>4100</td>\n",
              "      <td>1027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>US</th>\n",
              "      <td>345123</td>\n",
              "      <td>205069</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "state_lump  failed  successful\n",
              "country                       \n",
              "Other        19749        5210\n",
              "AU           10615        3460\n",
              "CA           19547        7201\n",
              "DE            5388        1467\n",
              "FR            3429        1420\n",
              "GB           39774       21407\n",
              "IT            3957         671\n",
              "N,0\"          3692         105\n",
              "NL            4100        1027\n",
              "US          345123      205069"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ceNFrtMOpYvS",
        "outputId": "d631fc9c-8bd9-4528-9ff1-ed2683b974a3"
      },
      "source": [
        "stats.chi2_contingency(country_crosstab)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8569.608152574416, 0.0, 9, array([[ 16180.95341047,   8778.04658953],\n",
              "        [  9124.84151017,   4950.15848983],\n",
              "        [ 17340.76452675,   9407.23547325],\n",
              "        [  4444.10575859,   2410.89424141],\n",
              "        [  3143.61324922,   1705.38675078],\n",
              "        [ 39663.72493312,  21517.27506688],\n",
              "        [  3000.33865073,   1627.66134927],\n",
              "        [  2461.60022836,   1335.39977164],\n",
              "        [  3323.8410247 ,   1803.1589753 ],\n",
              "        [356690.21670788, 193501.78329212]]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZAdTwWGkpYvT"
      },
      "source": [
        "Independence is rejected. Correlation between country and state of the project is significant. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "d2mmkorHpYvT",
        "outputId": "d9d9d6d9-832b-4a64-cfbd-0787eadbc39a"
      },
      "source": [
        "currency_crosstab = pd.crosstab(ks['currency'], ks['state_lump'])\n",
        "currency_crosstab\n"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>state_lump</th>\n",
              "      <th>failed</th>\n",
              "      <th>successful</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>currency</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>1614</td>\n",
              "      <td>447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AUD</th>\n",
              "      <td>10835</td>\n",
              "      <td>3462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CAD</th>\n",
              "      <td>19952</td>\n",
              "      <td>7207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CHF</th>\n",
              "      <td>973</td>\n",
              "      <td>273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DKK</th>\n",
              "      <td>1375</td>\n",
              "      <td>595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EUR</th>\n",
              "      <td>22863</td>\n",
              "      <td>6097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GBP</th>\n",
              "      <td>40665</td>\n",
              "      <td>21435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MXN</th>\n",
              "      <td>1568</td>\n",
              "      <td>398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NOK</th>\n",
              "      <td>990</td>\n",
              "      <td>272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NZD</th>\n",
              "      <td>1861</td>\n",
              "      <td>778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SEK</th>\n",
              "      <td>2244</td>\n",
              "      <td>844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>USD</th>\n",
              "      <td>350434</td>\n",
              "      <td>205229</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "state_lump  failed  successful\n",
              "currency                      \n",
              "Other         1614         447\n",
              "AUD          10835        3462\n",
              "CAD          19952        7207\n",
              "CHF            973         273\n",
              "DKK           1375         595\n",
              "EUR          22863        6097\n",
              "GBP          40665       21435\n",
              "MXN           1568         398\n",
              "NOK            990         272\n",
              "NZD           1861         778\n",
              "SEK           2244         844\n",
              "USD         350434      205229"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u_AzCO3-pYvU",
        "outputId": "9b41926e-982a-4ae0-e993-74d13e8ed1c9"
      },
      "source": [
        "stats.chi2_contingency(currency_crosstab)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5638.724200233166, 0.0, 11, array([[  1336.14908366,    724.85091634],\n",
              "        [  9268.76441001,   5028.23558999],\n",
              "        [ 17607.21638186,   9551.78361814],\n",
              "        [   807.78348289,    438.21651711],\n",
              "        [  1277.15366075,    692.84633925],\n",
              "        [ 18774.80711435,  10185.19288565],\n",
              "        [ 40259.51387436,  21840.48612564],\n",
              "        [  1274.56045535,    691.43954465],\n",
              "        [   818.1563045 ,    443.8436955 ],\n",
              "        [  1710.86726432,    928.13273568],\n",
              "        [  2001.95457076,   1086.04542924],\n",
              "        [360237.0733972 , 195425.9266028 ]]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rHBNStIOpYvU"
      },
      "source": [
        "as expected, currency and state of the project are not independent, so they are correlated. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "siKFlc9apYvU",
        "outputId": "baa5014c-5280-439c-eeb8-9e027a40a569"
      },
      "source": [
        "test_crosstab = pd.crosstab(ks['country'], ks['main_category'])\n",
        "test_crosstab"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>main_category</th>\n",
              "      <th>Other</th>\n",
              "      <th>Art</th>\n",
              "      <th>Design</th>\n",
              "      <th>Fashion</th>\n",
              "      <th>Film &amp; Video</th>\n",
              "      <th>Food</th>\n",
              "      <th>Games</th>\n",
              "      <th>Music</th>\n",
              "      <th>Photography</th>\n",
              "      <th>Publishing</th>\n",
              "      <th>Technology</th>\n",
              "      <th>Theater</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>1831</td>\n",
              "      <td>1397</td>\n",
              "      <td>2473</td>\n",
              "      <td>1494</td>\n",
              "      <td>2998</td>\n",
              "      <td>1279</td>\n",
              "      <td>2790</td>\n",
              "      <td>4040</td>\n",
              "      <td>570</td>\n",
              "      <td>2329</td>\n",
              "      <td>3441</td>\n",
              "      <td>317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AU</th>\n",
              "      <td>914</td>\n",
              "      <td>744</td>\n",
              "      <td>1604</td>\n",
              "      <td>1279</td>\n",
              "      <td>1731</td>\n",
              "      <td>897</td>\n",
              "      <td>1696</td>\n",
              "      <td>863</td>\n",
              "      <td>539</td>\n",
              "      <td>1477</td>\n",
              "      <td>2199</td>\n",
              "      <td>132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CA</th>\n",
              "      <td>2245</td>\n",
              "      <td>1944</td>\n",
              "      <td>2691</td>\n",
              "      <td>1852</td>\n",
              "      <td>3154</td>\n",
              "      <td>1956</td>\n",
              "      <td>3373</td>\n",
              "      <td>2153</td>\n",
              "      <td>749</td>\n",
              "      <td>2798</td>\n",
              "      <td>3473</td>\n",
              "      <td>360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DE</th>\n",
              "      <td>394</td>\n",
              "      <td>365</td>\n",
              "      <td>889</td>\n",
              "      <td>462</td>\n",
              "      <td>529</td>\n",
              "      <td>390</td>\n",
              "      <td>1072</td>\n",
              "      <td>499</td>\n",
              "      <td>361</td>\n",
              "      <td>481</td>\n",
              "      <td>1366</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FR</th>\n",
              "      <td>280</td>\n",
              "      <td>287</td>\n",
              "      <td>490</td>\n",
              "      <td>414</td>\n",
              "      <td>455</td>\n",
              "      <td>239</td>\n",
              "      <td>811</td>\n",
              "      <td>295</td>\n",
              "      <td>172</td>\n",
              "      <td>281</td>\n",
              "      <td>1096</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GB</th>\n",
              "      <td>4491</td>\n",
              "      <td>4801</td>\n",
              "      <td>4803</td>\n",
              "      <td>4230</td>\n",
              "      <td>10783</td>\n",
              "      <td>3003</td>\n",
              "      <td>7157</td>\n",
              "      <td>5054</td>\n",
              "      <td>2287</td>\n",
              "      <td>5892</td>\n",
              "      <td>5606</td>\n",
              "      <td>3074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IT</th>\n",
              "      <td>259</td>\n",
              "      <td>295</td>\n",
              "      <td>604</td>\n",
              "      <td>468</td>\n",
              "      <td>330</td>\n",
              "      <td>311</td>\n",
              "      <td>590</td>\n",
              "      <td>267</td>\n",
              "      <td>220</td>\n",
              "      <td>296</td>\n",
              "      <td>950</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>N,0\"</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>888</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>2388</td>\n",
              "      <td>1</td>\n",
              "      <td>495</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NL</th>\n",
              "      <td>252</td>\n",
              "      <td>344</td>\n",
              "      <td>589</td>\n",
              "      <td>484</td>\n",
              "      <td>388</td>\n",
              "      <td>333</td>\n",
              "      <td>717</td>\n",
              "      <td>348</td>\n",
              "      <td>183</td>\n",
              "      <td>359</td>\n",
              "      <td>1090</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>US</th>\n",
              "      <td>41315</td>\n",
              "      <td>41951</td>\n",
              "      <td>39796</td>\n",
              "      <td>30527</td>\n",
              "      <td>100008</td>\n",
              "      <td>37420</td>\n",
              "      <td>45028</td>\n",
              "      <td>82755</td>\n",
              "      <td>15377</td>\n",
              "      <td>59699</td>\n",
              "      <td>39469</td>\n",
              "      <td>16847</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "main_category  Other    Art  Design  ...  Publishing  Technology  Theater\n",
              "country                              ...                                 \n",
              "Other           1831   1397    2473  ...        2329        3441      317\n",
              "AU               914    744    1604  ...        1477        2199      132\n",
              "CA              2245   1944    2691  ...        2798        3473      360\n",
              "DE               394    365     889  ...         481        1366       47\n",
              "FR               280    287     490  ...         281        1096       29\n",
              "GB              4491   4801    4803  ...        5892        5606     3074\n",
              "IT               259    295     604  ...         296         950       38\n",
              "N,0\"               2      0       3  ...         495           7        1\n",
              "NL               252    344     589  ...         359        1090       40\n",
              "US             41315  41951   39796  ...       59699       39469    16847\n",
              "\n",
              "[10 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yMMgNisQpYvV",
        "outputId": "ab3b920a-dc03-41e3-8558-8f3b06214d17"
      },
      "source": [
        "stats.chi2_contingency(test_crosstab)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(31096.009521422,\n",
              " 0.0,\n",
              " 99,\n",
              " array([[ 1847.12895584,  1852.28128831,  1916.73874413,  1464.47055356,\n",
              "          4308.91340825,  1628.52792596,  2247.09208854,  3505.78914339,\n",
              "           726.97634433,  2633.26829022,  2085.69971569,   742.11354179],\n",
              "        [ 1041.64189485,  1044.54742309,  1080.89658334,   825.85131782,\n",
              "          2429.90329024,   918.36734476,  1267.19103915,  1977.00157031,\n",
              "           409.96001629,  1484.96539063,  1176.17787164,   418.49625789],\n",
              "        [ 1979.52663611,  1985.04827516,  2054.12588356,  1569.44021663,\n",
              "          4617.76576961,  1745.25681973,  2408.15814673,  3757.07552416,\n",
              "           779.08422846,  2822.01451287,  2235.19756382,   795.30642316],\n",
              "        [  507.31475589,   508.7298462 ,   526.43311395,   402.21746243,\n",
              "          1183.44490619,   447.27588976,   617.16480095,   962.86648415,\n",
              "           199.66436317,   723.22825952,   572.83831688,   203.82180091],\n",
              "        [  358.85765884,   359.85864686,   372.38135223,   284.51531368,\n",
              "           837.12973743,   316.38815309,   436.56194308,   681.09986603,\n",
              "           141.23595872,   511.58772143,   405.2068561 ,   144.1767925 ],\n",
              "        [ 4527.79344714,  4540.42315397,  4698.42514141,  3589.79818653,\n",
              "         10562.26736768,  3991.9454721 ,  5508.20710239,  8593.6009288 ,\n",
              "          1782.00808216,  6454.82540421,  5112.59242381,  1819.1132898 ],\n",
              "        [  342.50221594,   343.45758253,   355.40954797,   271.54812781,\n",
              "           798.97637138,   301.96831769,   416.66501806,   650.05778099,\n",
              "           134.79893111,   488.27139097,   386.7389833 ,   137.60573226],\n",
              "        [  281.00279039,   281.78661211,   291.59249214,   222.7891619 ,\n",
              "           655.51280945,   247.74712668,   341.84897873,   533.33392273,\n",
              "           110.59454223,   400.59776826,   317.29643898,   112.89735639],\n",
              "        [  379.43147388,   380.48984996,   393.73049966,   300.82697737,\n",
              "           885.12356441,   334.52713155,   461.59065419,   720.14828071,\n",
              "           149.33321517,   540.91776609,   428.43793591,   152.4426511 ],\n",
              "        [40717.80017112, 40831.37732182, 42252.26664161, 32282.54268228,\n",
              "         94984.96277536, 35898.99581869, 49534.52022819, 77281.02649873,\n",
              "         16025.34431835, 58047.32349579, 45976.81389386, 16359.0261542 ]]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a6c2azZvpYvW"
      },
      "source": [
        "Two sampled t-test Hypothesis: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wX9FxbEUpYvW"
      },
      "source": [
        "from scipy.stats import ttest_ind"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kkjEI-3OpYvW",
        "outputId": "c2566e73-87fb-4c6f-a113-3f27c9312967"
      },
      "source": [
        "scipy.stats.ttest_ind(ks['state_lump'] == 'failed', ks['state_lump'] == 'successful', equal_var=False)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=368.11340576261455, pvalue=0.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tw16BY46pYvX"
      },
      "source": [
        "from statsmodels.stats.multicomp import pairwise_tukeyhsd\n",
        "from statsmodels.stats.multicomp import MultiComparison"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IydcRUj4pYvX",
        "outputId": "3c463fb7-ff38-47cb-ffea-1c124b87fb04"
      },
      "source": [
        "ks.loc[:,'pledged'].describe()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     702411\n",
              "unique    112634\n",
              "top            0\n",
              "freq       52804\n",
              "Name: pledged, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DzPtwDwMpYvZ"
      },
      "source": [
        "test the mean of pledge difference for main categories or countries. pd.to_numeric(df4['Sales']).groupby(df4['Company'])"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kF7QmDAYpYvZ",
        "outputId": "09aa6100-7ddb-46fb-b83d-a76687a1d70a"
      },
      "source": [
        "# Try anova test longevity and pledged values.\n",
        "print(list(ks_dummy))"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['ID', 'name', 'category', 'deadline', 'goal', 'launched', 'pledged', 'state', 'backers', 'usd.pledged', 'launched_year', 'launched_month', 'launched_day', 'main_category_Art', 'main_category_Design', 'main_category_Fashion', 'main_category_Film & Video', 'main_category_Food', 'main_category_Games', 'main_category_Music', 'main_category_Photography', 'main_category_Publishing', 'main_category_Technology', 'main_category_Theater', 'state_lump_successful', 'currency_AUD', 'currency_CAD', 'currency_CHF', 'currency_DKK', 'currency_EUR', 'currency_GBP', 'currency_MXN', 'currency_NOK', 'currency_NZD', 'currency_SEK', 'currency_USD', 'country_AU', 'country_CA', 'country_DE', 'country_FR', 'country_GB', 'country_IT', 'country_N,0\"', 'country_NL', 'country_US', 'longevity']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NUC0n0aMpYvZ"
      },
      "source": [
        "ks7 = ks_dummy[['pledged', 'backers', 'longevity', 'goal']]\n",
        "ks7 = pd.DataFrame(ks7)\n",
        "ks7['backers'] = pd.to_numeric(ks7[ 'backers'] , errors='coerce')\n",
        "ks7['pledged'] = pd.to_numeric(ks7['pledged'] , errors='coerce')\n",
        "ks7['goal'] = pd.to_numeric(ks7[ 'goal'] , errors='coerce')\n",
        "\n"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "xaypP7w7pYva",
        "outputId": "d77e5b58-edfc-40b0-caed-fc61c845a184"
      },
      "source": [
        "sns.heatmap(ks7.corr(), annot=True)\n"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa196dc6d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hvE1y8chpYva"
      },
      "source": [
        "# Preprocessing and Modeling with sklearn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-_ODa3tgpYvb"
      },
      "source": [
        "from sklearn import svm\n",
        "from sklearn.feature_selection import SelectKBest, f_classif\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.feature_selection import SelectFromModel\n",
        "from sklearn import preprocessing\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split,GridSearchCV\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score,classification_report\n",
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XSuzZePYpYvb",
        "outputId": "cd77ffd4-3b87-4162-c054-359ae6f27b37"
      },
      "source": [
        "ks.isnull().sum()/ks.shape[0]\n",
        "#ks = ks.dropna(axis=0, subset=['name', 'category', 'main_category', 'currency', ])"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                0.000000\n",
              "name              0.000011\n",
              "category          0.000007\n",
              "main_category     0.000000\n",
              "currency          0.000000\n",
              "deadline          0.000900\n",
              "goal              0.000000\n",
              "launched          0.000830\n",
              "pledged           0.000000\n",
              "state             0.000000\n",
              "backers           0.000000\n",
              "country           0.000000\n",
              "usd.pledged       0.010801\n",
              "state_lump        0.000000\n",
              "launched_year     0.000830\n",
              "launched_month    0.000830\n",
              "launched_day      0.000830\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iF2N031SpYvc"
      },
      "source": [
        "# 1% of pledged data is NULL "
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zkJT6kHTpYvc",
        "outputId": "5b0e5e7b-91f1-4317-d6b5-d5a77b08556f"
      },
      "source": [
        "ks_dummy.isna().sum()"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                               0\n",
              "name                             8\n",
              "category                         5\n",
              "deadline                       632\n",
              "goal                             0\n",
              "launched                       583\n",
              "pledged                          0\n",
              "state                            0\n",
              "backers                          0\n",
              "usd.pledged                   7587\n",
              "launched_year                  583\n",
              "launched_month                 583\n",
              "launched_day                   583\n",
              "main_category_Art                0\n",
              "main_category_Design             0\n",
              "main_category_Fashion            0\n",
              "main_category_Film & Video       0\n",
              "main_category_Food               0\n",
              "main_category_Games              0\n",
              "main_category_Music              0\n",
              "main_category_Photography        0\n",
              "main_category_Publishing         0\n",
              "main_category_Technology         0\n",
              "main_category_Theater            0\n",
              "state_lump_successful            0\n",
              "currency_AUD                     0\n",
              "currency_CAD                     0\n",
              "currency_CHF                     0\n",
              "currency_DKK                     0\n",
              "currency_EUR                     0\n",
              "currency_GBP                     0\n",
              "currency_MXN                     0\n",
              "currency_NOK                     0\n",
              "currency_NZD                     0\n",
              "currency_SEK                     0\n",
              "currency_USD                     0\n",
              "country_AU                       0\n",
              "country_CA                       0\n",
              "country_DE                       0\n",
              "country_FR                       0\n",
              "country_GB                       0\n",
              "country_IT                       0\n",
              "country_N,0\"                     0\n",
              "country_NL                       0\n",
              "country_US                       0\n",
              "longevity                      632\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GjxSECMVpYvc"
      },
      "source": [
        "# Univariate Feature Selection Method with SelectKBest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BsiOabL7pYvd",
        "outputId": "6158cf30-691a-4db1-8649-61f5848a91e6"
      },
      "source": [
        "print(list(ks_dummy))"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['ID', 'name', 'category', 'deadline', 'goal', 'launched', 'pledged', 'state', 'backers', 'usd.pledged', 'launched_year', 'launched_month', 'launched_day', 'main_category_Art', 'main_category_Design', 'main_category_Fashion', 'main_category_Film & Video', 'main_category_Food', 'main_category_Games', 'main_category_Music', 'main_category_Photography', 'main_category_Publishing', 'main_category_Technology', 'main_category_Theater', 'state_lump_successful', 'currency_AUD', 'currency_CAD', 'currency_CHF', 'currency_DKK', 'currency_EUR', 'currency_GBP', 'currency_MXN', 'currency_NOK', 'currency_NZD', 'currency_SEK', 'currency_USD', 'country_AU', 'country_CA', 'country_DE', 'country_FR', 'country_GB', 'country_IT', 'country_N,0\"', 'country_NL', 'country_US', 'longevity']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kWHIuZmUpYvd"
      },
      "source": [
        "remove_col = ['ID', 'name', 'category', 'deadline','launched','state']\n",
        "ks_dummy2 = ks_dummy.drop(remove_col, axis=1) # removed the columns not among the predictors. \n"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0JBm0XTGpYvd",
        "outputId": "c85b80bc-5601-4647-929c-c63a963944eb"
      },
      "source": [
        "print(list(ks_dummy2))"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['goal', 'pledged', 'backers', 'usd.pledged', 'launched_year', 'launched_month', 'launched_day', 'main_category_Art', 'main_category_Design', 'main_category_Fashion', 'main_category_Film & Video', 'main_category_Food', 'main_category_Games', 'main_category_Music', 'main_category_Photography', 'main_category_Publishing', 'main_category_Technology', 'main_category_Theater', 'state_lump_successful', 'currency_AUD', 'currency_CAD', 'currency_CHF', 'currency_DKK', 'currency_EUR', 'currency_GBP', 'currency_MXN', 'currency_NOK', 'currency_NZD', 'currency_SEK', 'currency_USD', 'country_AU', 'country_CA', 'country_DE', 'country_FR', 'country_GB', 'country_IT', 'country_N,0\"', 'country_NL', 'country_US', 'longevity']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "SrYqGEZ-pYve",
        "outputId": "db99fc2a-1f91-4937-f1d0-81b214cf6a1b"
      },
      "source": [
        "ks_dummy2['pledged'] = pd.to_numeric(ks_dummy2['pledged'], errors='coerce') # converting column values to float\n",
        "ks_dummy2['backers'] = pd.to_numeric(ks_dummy2['backers'] , errors='coerce')\n",
        "ks_dummy2['goal'] = pd.to_numeric(ks_dummy2['goal'] , errors='coerce')\n",
        "ks_dummy2['usd.pledged'] = pd.to_numeric(ks_dummy2['usd.pledged'] , errors='coerce')\n",
        "ks_dummy2 = ks_dummy2.dropna() # SelectKBest can not process NaN values\n",
        "ks_dummy2.head()"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>goal</th>\n",
              "      <th>pledged</th>\n",
              "      <th>backers</th>\n",
              "      <th>usd.pledged</th>\n",
              "      <th>launched_year</th>\n",
              "      <th>launched_month</th>\n",
              "      <th>launched_day</th>\n",
              "      <th>main_category_Art</th>\n",
              "      <th>main_category_Design</th>\n",
              "      <th>main_category_Fashion</th>\n",
              "      <th>main_category_Film &amp; Video</th>\n",
              "      <th>main_category_Food</th>\n",
              "      <th>main_category_Games</th>\n",
              "      <th>main_category_Music</th>\n",
              "      <th>main_category_Photography</th>\n",
              "      <th>main_category_Publishing</th>\n",
              "      <th>main_category_Technology</th>\n",
              "      <th>main_category_Theater</th>\n",
              "      <th>state_lump_successful</th>\n",
              "      <th>currency_AUD</th>\n",
              "      <th>currency_CAD</th>\n",
              "      <th>currency_CHF</th>\n",
              "      <th>currency_DKK</th>\n",
              "      <th>currency_EUR</th>\n",
              "      <th>currency_GBP</th>\n",
              "      <th>currency_MXN</th>\n",
              "      <th>currency_NOK</th>\n",
              "      <th>currency_NZD</th>\n",
              "      <th>currency_SEK</th>\n",
              "      <th>currency_USD</th>\n",
              "      <th>country_AU</th>\n",
              "      <th>country_CA</th>\n",
              "      <th>country_DE</th>\n",
              "      <th>country_FR</th>\n",
              "      <th>country_GB</th>\n",
              "      <th>country_IT</th>\n",
              "      <th>country_N,0\"</th>\n",
              "      <th>country_NL</th>\n",
              "      <th>country_US</th>\n",
              "      <th>longevity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>59.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>2421.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>45000.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>45.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5000.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19500.0</td>\n",
              "      <td>1283.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1283.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>56.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      goal  pledged  backers  ...  country_NL  country_US  longevity\n",
              "0   1000.0      0.0      0.0  ...           0           0       59.0\n",
              "1  30000.0   2421.0     15.0  ...           0           1       60.0\n",
              "2  45000.0    220.0      3.0  ...           0           1       45.0\n",
              "3   5000.0      1.0      1.0  ...           0           1       30.0\n",
              "4  19500.0   1283.0     14.0  ...           0           1       56.0\n",
              "\n",
              "[5 rows x 40 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J4FkUyHfpYve"
      },
      "source": [
        "drop_col= ['state_lump_successful']\n",
        "X = ks_dummy2.drop(drop_col, axis =1) # dropped the target column, the rest wil be our predictors. \n",
        "y = ks_dummy2['state_lump_successful'] # selected the target column that will be predicted"
      ],
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G0K1D1WQpYve"
      },
      "source": [
        "# instantiate SelectKBest to determine 20 best features\n",
        "best_features = SelectKBest(score_func = f_classif, k=20)\n",
        "fit = best_features.fit(X,y)\n",
        "df_scores = pd.DataFrame(fit.scores_)\n",
        "df_columns = pd.DataFrame(X.columns)"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3q9gLxTLpYvf",
        "outputId": "67a697e5-b83d-4532-bced-00af4d14bded"
      },
      "source": [
        "# concatenate dataframes\n",
        "feature_scores = pd.concat([df_columns, df_scores], axis = 1)\n",
        "feature_scores.columns = ['Feature_Name', 'Score'] \n",
        "print(feature_scores.nlargest(20, 'Score')) # print 20 best features"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                Feature_Name         Score\n",
            "2                    backers  10853.523192\n",
            "13       main_category_Music   8368.781880\n",
            "1                    pledged   8238.745783\n",
            "4              launched_year   7782.534595\n",
            "16  main_category_Technology   7203.109865\n",
            "3                usd.pledged   6933.383438\n",
            "17     main_category_Theater   5657.835173\n",
            "28              currency_USD   3426.360573\n",
            "37                country_US   3426.360573\n",
            "22              currency_EUR   2654.952375\n",
            "9      main_category_Fashion   2542.629462\n",
            "11        main_category_Food   2500.433948\n",
            "19              currency_CAD    906.332939\n",
            "30                country_CA    906.332939\n",
            "34                country_IT    902.759043\n",
            "15  main_category_Publishing    789.669454\n",
            "18              currency_AUD    755.795418\n",
            "29                country_AU    755.795418\n",
            "7          main_category_Art    629.255956\n",
            "31                country_DE    605.974910\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ksZi7ZxpYvg"
      },
      "source": [
        "# Based on the features we can say; 'Number of backers', ' main category', 'pledged amount', 'launch time' and 'country' are the most important features of predictors.a"
      ],
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eg_dMaKDpYvg"
      },
      "source": [
        "# export selected features to .csv\n",
        "df_univ_feat = feature_scores.nlargest(20,'Score')\n",
        "df_univ_feat.to_csv('feature_selection_UNIVARIATE.csv', index=False)"
      ],
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2EZa5EfpYvg"
      },
      "source": [
        "# Feature Selection Using Feature Importance Method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7nmC2x5SpYvh"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0wsEpIfnpYvh",
        "outputId": "8b5e8bf3-0fee-4545-f0f7-c79938eb7a07"
      },
      "source": [
        "rf_model = RandomForestClassifier()\n",
        "rf_model.fit(X, y)"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sX2DX2SJpYvi",
        "outputId": "504e964f-3576-46ab-c478-a15ce838039f"
      },
      "source": [
        "feat_importances = pd.Series(rf_model.feature_importances_, index=X.columns)\n",
        "# determine 20 most important features\n",
        "df_imp_feat = feat_importances.nlargest(20)\n",
        "print(rf_model.feature_importances_)"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2.48819676e-01 2.56657949e-01 2.30073834e-01 1.84492895e-01\n",
            " 1.39951921e-02 8.41254253e-03 9.79690233e-03 1.39211402e-03\n",
            " 3.21211477e-03 1.06632385e-03 1.17937384e-03 1.16155199e-03\n",
            " 2.98125466e-03 4.68630821e-03 3.87645285e-04 8.14131062e-04\n",
            " 4.42361970e-03 2.22984556e-03 2.50770368e-04 3.83962523e-04\n",
            " 7.45527955e-05 2.28138115e-04 7.70378894e-04 6.55174903e-04\n",
            " 1.72716029e-04 1.24713605e-04 1.18469917e-04 3.12852709e-04\n",
            " 1.19271261e-03 2.61928936e-04 3.70998008e-04 2.02245053e-04\n",
            " 1.65607643e-04 7.56306905e-04 1.82258007e-04 0.00000000e+00\n",
            " 1.74521919e-04 1.07824880e-03 1.67401686e-02]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "id": "Mt96V8FTpYvi",
        "outputId": "b00f9b6e-81ab-4fb5-fcda-15abec9912a3"
      },
      "source": [
        "# plot 20 most important features\n",
        "# feat_importances.nlargest(20).plot(kind='barh')\n",
        "df_imp_feat.plot(kind='barh')\n",
        "plt.show()\n",
        "print(df_imp_feat)"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "pledged                       0.256658\n",
            "goal                          0.248820\n",
            "backers                       0.230074\n",
            "usd.pledged                   0.184493\n",
            "longevity                     0.016740\n",
            "launched_year                 0.013995\n",
            "launched_day                  0.009797\n",
            "launched_month                0.008413\n",
            "main_category_Music           0.004686\n",
            "main_category_Technology      0.004424\n",
            "main_category_Design          0.003212\n",
            "main_category_Games           0.002981\n",
            "main_category_Theater         0.002230\n",
            "main_category_Art             0.001392\n",
            "currency_USD                  0.001193\n",
            "main_category_Film & Video    0.001179\n",
            "main_category_Food            0.001162\n",
            "country_US                    0.001078\n",
            "main_category_Fashion         0.001066\n",
            "main_category_Publishing      0.000814\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HpDOqd4GpYvi"
      },
      "source": [
        "# Logistic Regression model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8nOr6g_UpYvj"
      },
      "source": [
        "# Based on the features we can say; 'Number of backers', ' main category', 'pledged amount', 'longevity' as duration of the campaign, 'launch time' and 'country' (US single most important country determines state of the project) are the most important features of predictors.\n",
        "logit_data = ks_dummy2[['state_lump_successful', 'pledged', 'backers', 'main_category_Art', 'main_category_Design', 'main_category_Fashion', 'main_category_Film & Video', 'main_category_Food', 'main_category_Games', 'main_category_Music', 'main_category_Photography', 'main_category_Publishing', 'main_category_Technology', 'main_category_Theater', 'country_US', 'launched_year', 'longevity']]\n"
      ],
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JTSD86CypYvj"
      },
      "source": [
        "# Spliting data\n",
        "train_X, test_X, train_y, test_y = train_test_split(logit_data.drop('state_lump_successful', axis=1), logit_data['state_lump_successful'], \n",
        "                                                    test_size=0.1, random_state=7)"
      ],
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2xsHdR7PpYvj",
        "outputId": "025dd99b-140d-444b-8e04-540397d7ae1d"
      },
      "source": [
        "# Creating model\n",
        "LR = LogisticRegression()\n",
        "\n",
        "# Fitting model\n",
        "LR.fit(train_X, train_y)\n",
        "\n",
        "# Scoring\n",
        "print(\"Model's accuracy is {0}%\".format(round(LR.score(test_X, test_y)*100, 2)))"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model's accuracy is 78.13%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "igOnw0RBpYvk",
        "outputId": "cfc9b7e7-2c95-40be-ab6f-8d2d56041560"
      },
      "source": [
        "from_largest = np.argsort(LR.coef_)[0][::-1]\n",
        "positive_coef_inds = []\n",
        "for index in from_largest:\n",
        "    if LR.coef_[0][index] > 0:\n",
        "        positive_coef_inds.append(index)\n",
        "    else:\n",
        "        break\n",
        "print(train_X.iloc[:, positive_coef_inds].columns)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['backers', 'main_category_Music', 'country_US', 'main_category_Theater',\n",
            "       'main_category_Art', 'main_category_Film & Video', 'pledged',\n",
            "       'main_category_Photography'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_uA5S3g3pYvk"
      },
      "source": [
        "## in our model , number of backers, main_category, country of origin, and pledged amount are the features had positive coefficients"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nqnK-tbnpYvk",
        "outputId": "f74ed08b-7c44-422c-bdf1-78e6398bd624"
      },
      "source": [
        "print(train_X.iloc[:, np.argmin(LR.coef_[0])].name)"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "longevity\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1EfBKH4GpYvl"
      },
      "source": [
        "# longevity as duration of campaign has the lowest coefficient so it contributed most for a project to fail. "
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S3e_3cJwpYvl"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers.core import Dense, Dropout, Activation, Flatten\n",
        "from keras.layers.noise import GaussianNoise\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.layers.advanced_activations import PReLU\n",
        "from keras.utils import np_utils\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aHTnAB4xpYvn"
      },
      "source": [
        "# Artificial neural network, ANN Model. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7tuoZVeHpYvo"
      },
      "source": [
        "drop_col= ['state_lump_successful']\n",
        "X = logit_data.drop(drop_col, axis =1) # dropped the target column, the rest wil be our predictors. \n",
        "y = logit_data['state_lump_successful'] # selected the target column that will be predicted"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jz8xmQgrpYvp"
      },
      "source": [
        "def ANN_training(X,y):\n",
        "    \n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)\n",
        "    \n",
        "    scaler = MinMaxScaler((-1,1))\n",
        "    X_train_scaled = scaler.fit_transform(X_train)\n",
        "    X_test_scaled = scaler.transform(X_test)\n",
        "    \n",
        "    clf = Sequential()\n",
        "    clf.add(Dense(input_shape = (X.shape[1],), units = 10, activation = 'relu'))\n",
        "    clf.add(Dense(units = 6, activation = 'relu'))\n",
        "    clf.add(Dense(units = 1, activation = 'sigmoid'))\n",
        "    clf.compile(optimizer = \"adam\", loss = \"binary_crossentropy\", metrics = ['accuracy'])\n",
        "    clf.fit(X_train, y_train, batch_size = 100, epochs = 10)\n",
        "\n",
        "    # Predicting results\n",
        "    y_pred = clf.predict(X_test, batch_size = 10)\n",
        "    y_pred = (y_pred > 0.5)\n",
        "\n",
        "    cm = confusion_matrix(y_test, y_pred)\n",
        "    print(cm)"
      ],
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xncWRa5GpYvp",
        "outputId": "88b9a498-569f-4741-e25c-29348f142df2"
      },
      "source": [
        "ANN_training(X,y)"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "5554/5554 [==============================] - 5s 962us/step - loss: 6.1215 - accuracy: 0.7969\n",
            "Epoch 2/10\n",
            "5554/5554 [==============================] - 5s 951us/step - loss: 1.6782 - accuracy: 0.8144\n",
            "Epoch 3/10\n",
            "5554/5554 [==============================] - 5s 938us/step - loss: 2.0784 - accuracy: 0.8014\n",
            "Epoch 4/10\n",
            "5554/5554 [==============================] - 5s 940us/step - loss: 0.6012 - accuracy: 0.8212\n",
            "Epoch 5/10\n",
            "5554/5554 [==============================] - 5s 956us/step - loss: 0.3587 - accuracy: 0.8423\n",
            "Epoch 6/10\n",
            "5554/5554 [==============================] - 5s 956us/step - loss: 0.3549 - accuracy: 0.8451\n",
            "Epoch 7/10\n",
            "5554/5554 [==============================] - 5s 952us/step - loss: 0.3530 - accuracy: 0.8472\n",
            "Epoch 8/10\n",
            "5554/5554 [==============================] - 5s 957us/step - loss: 0.3508 - accuracy: 0.8490\n",
            "Epoch 9/10\n",
            "5554/5554 [==============================] - 5s 973us/step - loss: 0.3503 - accuracy: 0.8494\n",
            "Epoch 10/10\n",
            "5554/5554 [==============================] - 5s 950us/step - loss: 0.3506 - accuracy: 0.8495\n",
            "[[75830 13562]\n",
            " [ 7641 41806]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "piGXPdIhpYvp"
      },
      "source": [
        "# Decision Tree Classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hR4zM6OupYvq",
        "outputId": "e082046e-2ce3-45d6-a87e-9900ba8d686e"
      },
      "source": [
        "decisionTree = DecisionTreeClassifier(random_state=76)\n",
        "decisionTree.fit(train_X,train_y)"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=76, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JLqkMR5zpYvq"
      },
      "source": [
        "treePredictions = decisionTree.predict(test_X)"
      ],
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G3JvgjwFpYvr",
        "outputId": "9270f749-e6a6-4635-f886-4bde93e66288"
      },
      "source": [
        "print(classification_report(test_y, treePredictions))"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.97      0.97     44659\n",
            "           1       0.95      0.95      0.95     24761\n",
            "\n",
            "    accuracy                           0.96     69420\n",
            "   macro avg       0.96      0.96      0.96     69420\n",
            "weighted avg       0.96      0.96      0.96     69420\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NYDIHWZNpYvs"
      },
      "source": [
        "Decision Tree classification gives 96% accuracy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FLY_2CiApYvs"
      },
      "source": [
        "# Random forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TSguJZ0opYvs",
        "outputId": "2a729481-51d9-47b3-af63-4c968e3584a6"
      },
      "source": [
        "forest = RandomForestClassifier(n_estimators=500, random_state=76)\n",
        "forest.fit(train_X, train_y)\n"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=500,\n",
              "                       n_jobs=None, oob_score=False, random_state=76, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ff2qrdlcpYvt",
        "outputId": "3d680958-c863-4ef4-d3da-9276ddaaf0b8"
      },
      "source": [
        "forestPredictions = forest.predict(test_X)\n",
        "print(confusion_matrix(test_y, forestPredictions))\n",
        "print(classification_report(test_y, forestPredictions))"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[43624  1035]\n",
            " [  871 23890]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.98      0.98     44659\n",
            "           1       0.96      0.96      0.96     24761\n",
            "\n",
            "    accuracy                           0.97     69420\n",
            "   macro avg       0.97      0.97      0.97     69420\n",
            "weighted avg       0.97      0.97      0.97     69420\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rafT6GF7pYvt"
      },
      "source": [
        "# RF gave 97% acc."
      ],
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B7pvbfdqpYvt"
      },
      "source": [
        ""
      ],
      "execution_count": 126,
      "outputs": []
    }
  ]
}