Financial Inclusion Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Financial Inclusion in Africa Zindi Competition\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember to read the competition details in the zindi platform, [Zindi Africa](https://zindi.africa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import important modules \n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns  \n",
    "import warnings\n",
    "np.random.seed(123)\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import data\n",
    "train_data = pd.read_csv('data/Train_v2.csv')\n",
    "test_data = pd.read_csv('data/Test_v2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data shape : (23524, 13)\n",
      "test data shape : (10086, 12)\n"
     ]
    }
   ],
   "source": [
    "# print shape \n",
    "print('train data shape :', train_data.shape)\n",
    "print('test data shape :', test_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above output show the number of rows and columns for train and test dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>uniqueid</th>\n",
       "      <th>bank_account</th>\n",
       "      <th>location_type</th>\n",
       "      <th>cellphone_access</th>\n",
       "      <th>household_size</th>\n",
       "      <th>age_of_respondent</th>\n",
       "      <th>gender_of_respondent</th>\n",
       "      <th>relationship_with_head</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>education_level</th>\n",
       "      <th>job_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>2018</td>\n",
       "      <td>uniqueid_1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>Female</td>\n",
       "      <td>Spouse</td>\n",
       "      <td>Married/Living together</td>\n",
       "      <td>Secondary education</td>\n",
       "      <td>Self employed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>2018</td>\n",
       "      <td>uniqueid_2</td>\n",
       "      <td>No</td>\n",
       "      <td>Rural</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>70</td>\n",
       "      <td>Female</td>\n",
       "      <td>Head of Household</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>No formal education</td>\n",
       "      <td>Government Dependent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>2018</td>\n",
       "      <td>uniqueid_3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>Male</td>\n",
       "      <td>Other relative</td>\n",
       "      <td>Single/Never Married</td>\n",
       "      <td>Vocational/Specialised training</td>\n",
       "      <td>Self employed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>2018</td>\n",
       "      <td>uniqueid_4</td>\n",
       "      <td>No</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "      <td>Female</td>\n",
       "      <td>Head of Household</td>\n",
       "      <td>Married/Living together</td>\n",
       "      <td>Primary education</td>\n",
       "      <td>Formally employed Private</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>2018</td>\n",
       "      <td>uniqueid_5</td>\n",
       "      <td>No</td>\n",
       "      <td>Urban</td>\n",
       "      <td>No</td>\n",
       "      <td>8</td>\n",
       "      <td>26</td>\n",
       "      <td>Male</td>\n",
       "      <td>Child</td>\n",
       "      <td>Single/Never Married</td>\n",
       "      <td>Primary education</td>\n",
       "      <td>Informally employed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  year    uniqueid bank_account location_type cellphone_access  \\\n",
       "0   Kenya  2018  uniqueid_1          Yes         Rural              Yes   \n",
       "1   Kenya  2018  uniqueid_2           No         Rural               No   \n",
       "2   Kenya  2018  uniqueid_3          Yes         Urban              Yes   \n",
       "3   Kenya  2018  uniqueid_4           No         Rural              Yes   \n",
       "4   Kenya  2018  uniqueid_5           No         Urban               No   \n",
       "\n",
       "   household_size  age_of_respondent gender_of_respondent  \\\n",
       "0               3                 24               Female   \n",
       "1               5                 70               Female   \n",
       "2               5                 26                 Male   \n",
       "3               5                 34               Female   \n",
       "4               8                 26                 Male   \n",
       "\n",
       "  relationship_with_head           marital_status  \\\n",
       "0                 Spouse  Married/Living together   \n",
       "1      Head of Household                  Widowed   \n",
       "2         Other relative     Single/Never Married   \n",
       "3      Head of Household  Married/Living together   \n",
       "4                  Child     Single/Never Married   \n",
       "\n",
       "                   education_level                   job_type  \n",
       "0              Secondary education              Self employed  \n",
       "1              No formal education       Government Dependent  \n",
       "2  Vocational/Specialised training              Self employed  \n",
       "3                Primary education  Formally employed Private  \n",
       "4                Primary education        Informally employed  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect train Data\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Open the VariableDefinition file to understand the meaning of each variable in this dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uniqueid</th>\n",
       "      <th>bank_account</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>uniqueid_7867 x Kenya</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>uniqueid_6722 x Kenya</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>uniqueid_6714 x Kenya</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>uniqueid_8103 x Kenya</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>uniqueid_8657 x Kenya</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                uniqueid  bank_account\n",
       "0  uniqueid_7867 x Kenya           1.0\n",
       "1  uniqueid_6722 x Kenya           0.0\n",
       "2  uniqueid_6714 x Kenya           1.0\n",
       "3  uniqueid_8103 x Kenya           1.0\n",
       "4  uniqueid_8657 x Kenya           1.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.read_csv('data/SubmissionFile.csv')\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explolatory Data Analysis \n",
    "\n",
    "This is the process of finding some insights from you dataset before create predictive models.\n",
    "\n",
    "**Note:** This is important steps in your Data science workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['country',\n",
       " 'year',\n",
       " 'uniqueid',\n",
       " 'bank_account',\n",
       " 'location_type',\n",
       " 'cellphone_access',\n",
       " 'household_size',\n",
       " 'age_of_respondent',\n",
       " 'gender_of_respondent',\n",
       " 'relationship_with_head',\n",
       " 'marital_status',\n",
       " 'education_level',\n",
       " 'job_type']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show list of columns \n",
    "list(train_data.columns)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23524 entries, 0 to 23523\n",
      "Data columns (total 13 columns):\n",
      " #   Column                  Non-Null Count  Dtype \n",
      "---  ------                  --------------  ----- \n",
      " 0   country                 23524 non-null  object\n",
      " 1   year                    23524 non-null  int64 \n",
      " 2   uniqueid                23524 non-null  object\n",
      " 3   bank_account            23524 non-null  object\n",
      " 4   location_type           23524 non-null  object\n",
      " 5   cellphone_access        23524 non-null  object\n",
      " 6   household_size          23524 non-null  int64 \n",
      " 7   age_of_respondent       23524 non-null  int64 \n",
      " 8   gender_of_respondent    23524 non-null  object\n",
      " 9   relationship_with_head  23524 non-null  object\n",
      " 10  marital_status          23524 non-null  object\n",
      " 11  education_level         23524 non-null  object\n",
      " 12  job_type                23524 non-null  object\n",
      "dtypes: int64(3), object(10)\n",
      "memory usage: 2.3+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "## show Some information about the dataset \n",
    "print(train_data.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The outshow shows the list of variables , sizes and data types in each variables. This will help you to know what feature engineering techniques you can apply."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "missing values: country                   0\n",
      "year                      0\n",
      "uniqueid                  0\n",
      "bank_account              0\n",
      "location_type             0\n",
      "cellphone_access          0\n",
      "household_size            0\n",
      "age_of_respondent         0\n",
      "gender_of_respondent      0\n",
      "relationship_with_head    0\n",
      "marital_status            0\n",
      "education_level           0\n",
      "job_type                  0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values\n",
    "print('missing values:', train_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't have missing data in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a460021e80>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore Target distribution \n",
    "\n",
    "sns.catplot(x=\"bank_account\", kind=\"count\", data=train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     20212\n",
       "Yes     3312\n",
       "Name: bank_account, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['bank_account'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data shows that we have large number of **no** class than **yes** class  in our target variable "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In my previous article , I explained more about Exploratory data analysis with the financial Inclusion in Africa dataset.You can read and download the notebook [here](https://medium.com/analytics-vidhya/why-you-need-to-explore-your-data-how-you-can-start-13de6f29c8c1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>uniqueid</th>\n",
       "      <th>location_type</th>\n",
       "      <th>cellphone_access</th>\n",
       "      <th>household_size</th>\n",
       "      <th>age_of_respondent</th>\n",
       "      <th>gender_of_respondent</th>\n",
       "      <th>relationship_with_head</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>education_level</th>\n",
       "      <th>job_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>2018</td>\n",
       "      <td>uniqueid_1</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>Female</td>\n",
       "      <td>Spouse</td>\n",
       "      <td>Married/Living together</td>\n",
       "      <td>Secondary education</td>\n",
       "      <td>Self employed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>2018</td>\n",
       "      <td>uniqueid_2</td>\n",
       "      <td>Rural</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>70</td>\n",
       "      <td>Female</td>\n",
       "      <td>Head of Household</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>No formal education</td>\n",
       "      <td>Government Dependent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>2018</td>\n",
       "      <td>uniqueid_3</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>Male</td>\n",
       "      <td>Other relative</td>\n",
       "      <td>Single/Never Married</td>\n",
       "      <td>Vocational/Specialised training</td>\n",
       "      <td>Self employed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>2018</td>\n",
       "      <td>uniqueid_4</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "      <td>Female</td>\n",
       "      <td>Head of Household</td>\n",
       "      <td>Married/Living together</td>\n",
       "      <td>Primary education</td>\n",
       "      <td>Formally employed Private</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>2018</td>\n",
       "      <td>uniqueid_5</td>\n",
       "      <td>Urban</td>\n",
       "      <td>No</td>\n",
       "      <td>8</td>\n",
       "      <td>26</td>\n",
       "      <td>Male</td>\n",
       "      <td>Child</td>\n",
       "      <td>Single/Never Married</td>\n",
       "      <td>Primary education</td>\n",
       "      <td>Informally employed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  year    uniqueid location_type cellphone_access  household_size  \\\n",
       "0   Kenya  2018  uniqueid_1         Rural              Yes               3   \n",
       "1   Kenya  2018  uniqueid_2         Rural               No               5   \n",
       "2   Kenya  2018  uniqueid_3         Urban              Yes               5   \n",
       "3   Kenya  2018  uniqueid_4         Rural              Yes               5   \n",
       "4   Kenya  2018  uniqueid_5         Urban               No               8   \n",
       "\n",
       "   age_of_respondent gender_of_respondent relationship_with_head  \\\n",
       "0                 24               Female                 Spouse   \n",
       "1                 70               Female      Head of Household   \n",
       "2                 26                 Male         Other relative   \n",
       "3                 34               Female      Head of Household   \n",
       "4                 26                 Male                  Child   \n",
       "\n",
       "            marital_status                  education_level  \\\n",
       "0  Married/Living together              Secondary education   \n",
       "1                  Widowed              No formal education   \n",
       "2     Single/Never Married  Vocational/Specialised training   \n",
       "3  Married/Living together                Primary education   \n",
       "4     Single/Never Married                Primary education   \n",
       "\n",
       "                    job_type  \n",
       "0              Self employed  \n",
       "1       Government Dependent  \n",
       "2              Self employed  \n",
       "3  Formally employed Private  \n",
       "4        Informally employed  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import preprocessing module \n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler \n",
    "\n",
    "# Convert target label to numerical Data\n",
    "le = LabelEncoder()\n",
    "train_data['bank_account'] = le.fit_transform(train_data['bank_account'])\n",
    "\n",
    "#Separate training features from target\n",
    "X_train = train_data.drop(['bank_account'], axis=1)\n",
    "y_train = train_data['bank_account']\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    0\n",
       "Name: bank_account, dtype: int32"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# our target \n",
    "y_train.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have created a simple preprocessing function to handle \n",
    "\n",
    "- conversion of data types\n",
    "- convert categorical features to numerical by one-hot encoding method and label encoding\n",
    "- drop uniqueid variable \n",
    "- scaling our data into range of 0 and 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to preprocess our data from train models\n",
    "\n",
    "def preprocessing_data(data):\n",
    "\n",
    "    # Convert the following numerical labels from integer to float\n",
    "    float_array = data[[\"household_size\", \"age_of_respondent\", \"year\"]].values.astype(\n",
    "        float\n",
    "    )\n",
    "\n",
    "    # categorical features to be converted by One Hot Encoding\n",
    "    categ = [\n",
    "        \"relationship_with_head\",\n",
    "        \"marital_status\",\n",
    "        \"education_level\",\n",
    "        \"job_type\",\n",
    "        \"country\",\n",
    "    ]\n",
    "\n",
    "    # One Hot Encoding conversion\n",
    "    data = pd.get_dummies(data, prefix_sep=\"_\", columns=categ)\n",
    "\n",
    "    # Label Encoder conversion\n",
    "    data[\"location_type\"] = le.fit_transform(data[\"location_type\"])\n",
    "    data[\"cellphone_access\"] = le.fit_transform(data[\"cellphone_access\"])\n",
    "    data[\"gender_of_respondent\"] = le.fit_transform(data[\"gender_of_respondent\"])\n",
    "\n",
    "    # drop uniquid column\n",
    "    data = data.drop([\"uniqueid\"], axis=1)\n",
    "\n",
    "    # scale our data into range of 0 and 1\n",
    "    scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "    data = scaler.fit_transform(data)\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocess the train data \n",
    "processed_train_data = preprocessing_data(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.       , 0.       , 1.       , 0.1      , 0.0952381, 0.       ,\n",
       "        0.       , 0.       , 0.       , 0.       , 0.       , 1.       ,\n",
       "        0.       , 0.       , 1.       , 0.       , 0.       , 0.       ,\n",
       "        0.       , 0.       , 1.       , 0.       , 0.       , 0.       ,\n",
       "        0.       , 0.       , 0.       , 0.       , 0.       , 0.       ,\n",
       "        0.       , 0.       , 1.       , 1.       , 0.       , 0.       ,\n",
       "        0.       ]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the first train row \n",
    "processed_train_data[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocess the test data\n",
    "processed_test_data = preprocessing_data(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 1.        , 1.        , 0.10526316, 0.16666667,\n",
       "        1.        , 0.        , 1.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 1.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        1.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        1.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 1.        , 0.        ,\n",
       "        0.        , 0.        ]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the first test row\n",
    "processed_test_data[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23524, 37)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of the processed train set\n",
    "processed_train_data.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10086, 37)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of the processed test set\n",
    "processed_test_data.shape "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Building and Experiment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split train_data\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_Train, X_val, y_Train, y_val = train_test_split(processed_train_data, y_train, stratify = y_train,\n",
    "                                                  test_size = 0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
       "              learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
       "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
       "              nthread=None, objective='binary:logistic', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "              silent=None, subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import classifier algorithm here \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from xgboost import XGBClassifier \n",
    "\n",
    "# create models \n",
    "lg_model = LogisticRegression()\n",
    "rf_model = RandomForestClassifier()\n",
    "kn_model = KNeighborsClassifier()\n",
    "et_model = ExtraTreesClassifier()\n",
    "xg_model = XGBClassifier()\n",
    "\n",
    "\n",
    "#fitting the models\n",
    "lg_model.fit(X_Train,y_Train)\n",
    "rf_model.fit(X_Train,y_Train)\n",
    "kn_model.fit(X_Train,y_Train)\n",
    "et_model.fit(X_Train,y_Train)\n",
    "xg_model.fit(X_Train,y_Train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error rate of Logistic Regression classifier:  0.11219719507012327\n",
      "Error rate of Random Forest classifier:  0.1368465788355291\n",
      "Error rate of KNeighbors Classifier:  0.12027199320017001\n",
      "Error rate of Extra Tree classifier:  0.14747131321716955\n",
      "Error rate of XGB classifier:  0.11049723756906082\n"
     ]
    }
   ],
   "source": [
    "# import evaluation metrics\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "\n",
    "# evaluate the model\n",
    "lg_y_pred = lg_model.predict(X_val)\n",
    "rf_y_pred = rf_model.predict(X_val)\n",
    "kn_y_pred = kn_model.predict(X_val)\n",
    "et_y_pred = et_model.predict(X_val)\n",
    "xg_y_pred = xg_model.predict(X_val)\n",
    "\n",
    "# Get error rate\n",
    "print(\"Error rate of Logistic Regression classifier: \", 1 - accuracy_score(y_val, lg_y_pred))\n",
    "print(\"Error rate of Random Forest classifier: \", 1 - accuracy_score(y_val, rf_y_pred))\n",
    "print(\"Error rate of KNeighbors Classifier: \", 1 - accuracy_score(y_val, kn_y_pred))\n",
    "print(\"Error rate of Extra Tree classifier: \", 1 - accuracy_score(y_val, et_y_pred))\n",
    "print(\"Error rate of XGB classifier: \", 1 - accuracy_score(y_val, xg_y_pred))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XGB Classifier performs better than others classifiers.\n",
    "\n",
    "let's check the confusion matrix for XGB Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1a463447fd0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "# Get confusion matrix for Gradient Boosting Classifier \n",
    "plot_confusion_matrix( xg_model,X_val, y_val,normalize='true')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import GridSearchCV\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  5.5min\n",
      "[Parallel(n_jobs=-1)]: Done 180 out of 180 | elapsed:  6.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'gamma': 1, 'max_depth': 5, 'min_child_weight': 1, 'subsample': 0.8}\n"
     ]
    }
   ],
   "source": [
    "# Optimize model paramaters \n",
    "# I run this code in google colab to make the execution much faster and use the best params in the next code\n",
    "param_grid = {'min_child_weight': [1, 5, 10],\n",
    "        'gamma': [0.5, 1],\n",
    "        'subsample': [0.6, 0.8, 1.0],\n",
    "        'max_depth': [3,5]\n",
    "        }\n",
    "my_xgb_model = GridSearchCV(xg_model, param_grid,n_jobs=-1,verbose=2,cv=5)\n",
    "my_xgb_model.fit(X_Train, y_Train)\n",
    "print(my_xgb_model.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error rate of the  XGB classifier:  0.10837229069273269\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "\n",
    "\n",
    "# fit by setting best parameters and Evaluate model\n",
    "xgb_model = XGBClassifier(min_child_weight=1, gamma=1, subsample=0.8, max_depth=5)\n",
    "\n",
    "xgb_model.fit(X_Train, y_Train)\n",
    "y_pred = xgb_model.predict(X_val)\n",
    "\n",
    "# Get error rate\n",
    "print(\"Error rate of the  XGB classifier: \", 1 - accuracy_score(y_val, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model has been improved "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the predicted result for the test Data\n",
    "test_data.bank_account = xgb_model.predict(processed_test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create submission DataFrame\n",
    "submission = pd.DataFrame({\"uniqueid\": test_data[\"uniqueid\"] + \" x \" + test_data[\"country\"],\n",
    "\n",
    "\"bank_account\": test_data.bank_account})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "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>uniqueid</th>\n",
       "      <th>bank_account</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>uniqueid_6484 x Kenya</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5296</th>\n",
       "      <td>uniqueid_11431 x Rwanda</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6820</th>\n",
       "      <td>uniqueid_7095 x Tanzania</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4300</th>\n",
       "      <td>uniqueid_10435 x Rwanda</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8934</th>\n",
       "      <td>uniqueid_9209 x Tanzania</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      uniqueid  bank_account\n",
       "415      uniqueid_6484 x Kenya             0\n",
       "5296   uniqueid_11431 x Rwanda             0\n",
       "6820  uniqueid_7095 x Tanzania             0\n",
       "4300   uniqueid_10435 x Rwanda             0\n",
       "8934  uniqueid_9209 x Tanzania             0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show the five sample\n",
    "submission.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create submission csv file\n",
    "submission.to_csv('data/submissions/first_submission.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upload your **first_submission.csv** in the zindi competition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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