hw1.ipynb

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        "<a href=\"https://colab.research.google.com/github/wei-jun7/DataAnalytics2023_WeijunLi/blob/main/hw1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "Task 1 (10 points): Describe a machine learning problem that you would like to solve using\n",
        "Logistic Regression. Clearly state why Logistic regression is the best choice for solving this\n",
        "problem"
      ],
      "metadata": {
        "id": "d161X7t8tlOS"
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      "source": [
        "The Machine learing problem I want to solove is apple Apple Quality which it is a binary classification. There have 7 feature to affect the quality of the apple, such as size, Weight, Sweetness and so on. The most important thing is  the Quality is the binary with it is 0 or 1. 1 for good and 0 for bad. As the logistic regression the result require between 0 to 1 which our data is best fit on it. Moreover, it goes beyond mere classification by estimating the likelihood of each category, offering a nuanced view of results, especially useful for apples that don't clearly fall into one group. Additionally, the model's interpretability is a key strength. It clearly indicates which characteristics most significantly impact apple quality, making logistic regression not only effective but also insightful for this classification challenge.\n",
        "\n"
      ],
      "metadata": {
        "id": "WAZQcgj3tpaA"
      }
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      "cell_type": "markdown",
      "source": [
        "Task 2 (20 points): Pick a publicly available dataset (except The Titanic Dataset) that you will\n",
        "use to solve this problem. You must provide a link to the dataset and perform the necessary\n",
        "Exploratory Data Analysis (EDA). Clearly demonstrate the steps you follow for your EDA with a\n",
        "justification of why these were required. For example, if the dataset has lot of missing values,\n",
        "then why did you use a specific technique when handling missing data. This task may include\n",
        "data visualization"
      ],
      "metadata": {
        "id": "xx10dBTf_QWc"
      }
    },
    {
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "data = pd.read_csv('/content/apple_quality.csv')\n",
        "\n"
      ],
      "cell_type": "code",
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        "id": "c5bB3OSdD4xS"
      },
      "execution_count": 4,
      "outputs": []
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      "cell_type": "markdown",
      "source": [
        "The first step is read the data into the vector array which I will use the pandas to do it."
      ],
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        "id": "e0uY8bHgNlua"
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    {
      "cell_type": "code",
      "source": [
        "\n",
        "data.isna().sum()  # As we can see the last one is empty so we should remove it.\n",
        "print(data.shape)\n",
        "row = data.iloc[4000]\n",
        "print(row)\n",
        "data = data.iloc[:4000]\n",
        "print(data.shape)\n",
        "\n",
        "data = data.iloc[:4000,:]\n",
        "data.describe()\n",
        "\n"
      ],
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      "execution_count": 5,
      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4001, 9)\n",
            "A_id                                         NaN\n",
            "Size                                         NaN\n",
            "Weight                                       NaN\n",
            "Sweetness                                    NaN\n",
            "Crunchiness                                  NaN\n",
            "Juiciness                                    NaN\n",
            "Ripeness                                     NaN\n",
            "Acidity        Created_by_Nidula_Elgiriyewithana\n",
            "Quality                                      NaN\n",
            "Name: 4000, dtype: object\n",
            "(4000, 9)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "              A_id         Size       Weight    Sweetness  Crunchiness  \\\n",
              "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000   \n",
              "mean   1999.500000    -0.503015    -0.989547    -0.470479     0.985478   \n",
              "std    1154.844867     1.928059     1.602507     1.943441     1.402757   \n",
              "min       0.000000    -7.151703    -7.149848    -6.894485    -6.055058   \n",
              "25%     999.750000    -1.816765    -2.011770    -1.738425     0.062764   \n",
              "50%    1999.500000    -0.513703    -0.984736    -0.504758     0.998249   \n",
              "75%    2999.250000     0.805526     0.030976     0.801922     1.894234   \n",
              "max    3999.000000     6.406367     5.790714     6.374916     7.619852   \n",
              "\n",
              "         Juiciness     Ripeness  \n",
              "count  4000.000000  4000.000000  \n",
              "mean      0.512118     0.498277  \n",
              "std       1.930286     1.874427  \n",
              "min      -5.961897    -5.864599  \n",
              "25%      -0.801286    -0.771677  \n",
              "50%       0.534219     0.503445  \n",
              "75%       1.835976     1.766212  \n",
              "max       7.364403     7.237837  "
            ],
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              "      <th>A_id</th>\n",
              "      <th>Size</th>\n",
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              "      <th>Sweetness</th>\n",
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              "      <th>count</th>\n",
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              "      <td>4000.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>1999.500000</td>\n",
              "      <td>-0.503015</td>\n",
              "      <td>-0.989547</td>\n",
              "      <td>-0.470479</td>\n",
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              "      <th>std</th>\n",
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              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
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              "      <td>-5.864599</td>\n",
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              "      <th>25%</th>\n",
              "      <td>999.750000</td>\n",
              "      <td>-1.816765</td>\n",
              "      <td>-2.011770</td>\n",
              "      <td>-1.738425</td>\n",
              "      <td>0.062764</td>\n",
              "      <td>-0.801286</td>\n",
              "      <td>-0.771677</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1999.500000</td>\n",
              "      <td>-0.513703</td>\n",
              "      <td>-0.984736</td>\n",
              "      <td>-0.504758</td>\n",
              "      <td>0.998249</td>\n",
              "      <td>0.534219</td>\n",
              "      <td>0.503445</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2999.250000</td>\n",
              "      <td>0.805526</td>\n",
              "      <td>0.030976</td>\n",
              "      <td>0.801922</td>\n",
              "      <td>1.894234</td>\n",
              "      <td>1.835976</td>\n",
              "      <td>1.766212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3999.000000</td>\n",
              "      <td>6.406367</td>\n",
              "      <td>5.790714</td>\n",
              "      <td>6.374916</td>\n",
              "      <td>7.619852</td>\n",
              "      <td>7.364403</td>\n",
              "      <td>7.237837</td>\n",
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          "metadata": {},
          "execution_count": 5
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Second step find any NAN data in the dataset I found out the last row is use for author name with we should delete this row which affect the model accuracy. I use the the iloc to remove it and use the describe() function to see summary of each feature."
      ],
      "metadata": {
        "id": "eAP3Q9ZcOKTS"
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    {
      "cell_type": "code",
      "source": [
        "print(data.columns)\n",
        "\n",
        "data = data.drop(labels='A_id',axis=1)# as we know the id number should not be the model with with affect the model accuracy\n",
        "type(data['Acidity'][0])\n",
        "data['Acidity']=data['Acidity'].astype(float)#it origin is str but we should change to float with let it show in the graph\n",
        "fig, axs=plt.subplots(nrows=2,ncols=4,figsize=(20,8))\n",
        "axs = axs.flatten()\n",
        "for i, column in enumerate(data.columns):\n",
        "    sns.histplot(data[column], kde=True, ax=axs[i])\n",
        "    axs[i].set_title(f'Distplot of {column}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "44ADIqx6GatE",
        "outputId": "0b0bc01c-5069-401e-fed5-e3b55dc33224"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['A_id', 'Size', 'Weight', 'Sweetness', 'Crunchiness', 'Juiciness',\n",
            "       'Ripeness', 'Acidity', 'Quality'],\n",
            "      dtype='object')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x800 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I remove the A_id with it indicate each sample in our dataset but it is not import with we have the row to indicate the each sample. Next, I creat the each feature to see the disturbution. As we can see most all the feature expect the quality have the normal disturbution. It is a good sign with indicate our model can handel the target value with good quality and bad quality."
      ],
      "metadata": {
        "id": "bdTiZCa5O0-V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_cor = data.corr()\n",
        "plt.figure(figsize=(8,8))\n",
        "cmap_rb = sns.color_palette(\"Reds\", 10)\n",
        "sns.heatmap(data_cor,cmap=cmap_rb,annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 809
        },
        "id": "puIAX-fsL9om",
        "outputId": "8a968969-db52-4cf9-d676-c5fca402ce1a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-7c6f6bed14e8>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  data_cor = data.corr()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I creat the one heat map to see the each feature relationship with we can see teh the strongest postive relationship is size and acidity which mean that the size bigger the acidity stronger. The strongest negtive realtionis is size and sweetness which bigger size less sweetness. In overall, each feature is not have strong relationship which it better to use the logistic regression to find out the relationship between each feature."
      ],
      "metadata": {
        "id": "xnyB-gdCQLPG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Task 3 (25 points): Implement, Logistic Regression in this step. Clearly write your cost function\n",
        "and derivatives before implementing gradient descent. Do not use any built-in packages for this\n",
        "step. You can use the vectorization techniques demonstrated in class. Implement any 2 variants\n",
        "of gradient descent in their original form. (Refer to the research paper discussed in class).\n"
      ],
      "metadata": {
        "id": "aqLZxxdLRZyv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "def sigmoid(z):\n",
        "    # Ensure z is a NumPy array\n",
        "    z = np.array(z, dtype=float)\n",
        "\n",
        "    # Prevent numerical overflow\n",
        "    z_safe = np.clip(z, -500, 500)\n",
        "    return 1 / (1 + np.exp(-z_safe))\n",
        "\n",
        "def compute_cost(X, y, theta):\n",
        "    m = y.size\n",
        "    h = sigmoid(X.dot(theta))\n",
        "    cost = -1/m * (np.dot(y, np.log(h)) + np.dot(1 - y, np.log(1 - h)))\n",
        "    return cost\n",
        "\n",
        "def batch_gradient_descent(X, y, theta, alpha, iterations):\n",
        "    m = y.size\n",
        "\n",
        "    # Ensure y is a 1D array if it's a 2D array with a single column\n",
        "    if y.ndim > 1:\n",
        "        y = y.ravel()\n",
        "\n",
        "    cost_history = np.zeros(iterations)\n",
        "\n",
        "    for i in range(iterations):\n",
        "        h = sigmoid(X.dot(theta))\n",
        "        gradient = (1/m) * X.T.dot(h - y)\n",
        "\n",
        "        # Debugging print statements\n",
        "        # print(\"Shape of theta:\", theta.shape, \"Type of theta:\", theta.dtype)\n",
        "        # print(\"Shape of gradient:\", gradient.shape, \"Type of gradient:\", gradient.dtype)\n",
        "\n",
        "        theta -= alpha * gradient\n",
        "        cost_history[i] = compute_cost(X, y, theta)\n",
        "\n",
        "    return theta, cost_history\n",
        "\n",
        "\n",
        "# Data preprocessing\n",
        "data['Quality'] = np.where(data['Quality'] == 'good',1,0)\n",
        "dataset = data.to_numpy()\n",
        "np.random.shuffle(dataset)\n",
        "X = dataset[:, :-1]\n",
        "y = dataset[:, -1]\n",
        "split_size = int(0.8 * len(X))\n",
        "X_train, X_test = X[:split_size], X[split_size:]\n",
        "y_train, y_test = y[:split_size], y[split_size:]\n",
        "# y_train = np.where(y_train == 'good', 1, 0)\n",
        "# y_test = np.where(y_test == 'good', 1, 0)\n",
        "\n",
        "# Convert to numpy arrays and reshape if necessary\n",
        "X_train = np.array(X_train)\n",
        "y_train = np.array(y_train).reshape(-1, 1)\n",
        "X_test = np.array(X_test)\n",
        "y_test = np.array(y_test).reshape(-1, 1)\n",
        "\n",
        "# Add an intercept term to X_train and X_test\n",
        "X_train = np.column_stack((np.ones(X_train.shape[0]), X_train))\n",
        "X_test = np.column_stack((np.ones(X_test.shape[0]), X_test))\n",
        "\n",
        "# Initialize parameters\n",
        "theta = np.zeros(X_train.shape[1])\n",
        "alpha = 0.01\n",
        "iterations = 1000\n",
        "\n",
        "# Train the model\n",
        "theta_final, cost_history = batch_gradient_descent(X_train, y_train, theta, alpha, iterations)\n",
        "\n",
        "# Output the final parameters and cost history\n",
        "print(\"Final theta:\", theta_final)\n",
        "print(\"Final cost:\", cost_history[-1])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yhinbctFUL_K",
        "outputId": "900a8b4d-3145-4c40-bbef-d776425a9be9"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final theta: [ 0.26643388  0.50421295  0.1465876   0.43766949  0.07480835  0.42872291\n",
            " -0.15474056 -0.25102674]\n",
            "Final cost: 0.5228254395946723\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = sigmoid(np.dot(X_test, theta_final)) >= 0.5\n",
        "accuracy = np.mean(predictions == y_test)\n",
        "print(\"Accuracy:\", accuracy)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SrYKZaOsUcee",
        "outputId": "29b5d7ef-73a9-4b82-998f-e1bdb2b7ec11"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.49991875\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "def sigmoid(z):\n",
        "    # Ensure z is a NumPy array\n",
        "    z = np.array(z, dtype=float)\n",
        "\n",
        "    # Prevent numerical overflow\n",
        "    z_safe = np.clip(z, -500, 500)\n",
        "    return 1 / (1 + np.exp(-z_safe))\n",
        "\n",
        "def compute_cost(X, y, theta):\n",
        "    m = y.size\n",
        "    h = sigmoid(X.dot(theta))\n",
        "    cost = -1/m * (np.dot(y, np.log(h)) + np.dot(1 - y, np.log(1 - h)))\n",
        "    return cost\n",
        "\n",
        "def stochastic_gradient_descent(X, y, theta, alpha, iterations):\n",
        "    m = y.size\n",
        "\n",
        "    # Ensure y is a 1D array if it's a 2D array with a single column\n",
        "    if y.ndim > 1:\n",
        "        y = y.ravel()\n",
        "\n",
        "    cost_history = np.zeros(iterations)\n",
        "\n",
        "    for it in range(iterations):\n",
        "        # Shuffle the data for each iteration\n",
        "        indices = np.arange(m)\n",
        "        np.random.shuffle(indices)\n",
        "        X = X[indices]\n",
        "        y = y[indices]\n",
        "\n",
        "        for i in range(m):\n",
        "            xi = X[i, :].reshape(1, -1)\n",
        "            yi = y[i]\n",
        "            hi = sigmoid(xi.dot(theta))\n",
        "            gradient = xi.T * (hi - yi)\n",
        "            theta -= alpha * gradient\n",
        "\n",
        "            # Optionally, compute cost (can be omitted for faster performance)\n",
        "            cost_history[it] = compute_cost(X, y, theta)\n",
        "\n",
        "    return theta, cost_history\n",
        "\n",
        "\n",
        "# Data preprocessing\n",
        "data['Quality'] = np.where(data['Quality'] == 'good',1,0)\n",
        "dataset = data.to_numpy()\n",
        "np.random.shuffle(dataset)\n",
        "X = dataset[:, :-1]\n",
        "y = dataset[:, -1]\n",
        "split_size = int(0.8 * len(X))\n",
        "X_train, X_test = X[:split_size], X[split_size:]\n",
        "y_train, y_test = y[:split_size], y[split_size:]\n",
        "# y_train = np.where(y_train == 'good', 1, 0)\n",
        "# y_test = np.where(y_test == 'good', 1, 0)\n",
        "\n",
        "# Convert to numpy arrays and reshape if necessary\n",
        "X_train = np.array(X_train)\n",
        "y_train = np.array(y_train).reshape(-1, 1)\n",
        "X_test = np.array(X_test)\n",
        "y_test = np.array(y_test).reshape(-1, 1)\n",
        "\n",
        "# Add an intercept term to X_train and X_test\n",
        "X_train = np.column_stack((np.ones(X_train.shape[0]), X_train))\n",
        "X_test = np.column_stack((np.ones(X_test.shape[0]), X_test))\n",
        "\n",
        "# Initialize parameters\n",
        "theta = np.zeros(X_train.shape[1])\n",
        "alpha = 0.01\n",
        "iterations = 1000\n",
        "\n",
        "# Train the model\n",
        "theta_final, cost_history = batch_gradient_descent(X_train, y_train, theta, alpha, iterations)\n",
        "\n",
        "# Output the final parameters and cost history\n",
        "print(\"Final theta:\", theta_final)\n",
        "print(\"Final cost:\", cost_history[-1])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a9gXt5HofZ3t",
        "outputId": "94e18f95-e4ee-4279-dd37-5866b5246166"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final theta: [-1.48805518  0.40074141  0.47018907  0.30675729 -0.63019801 -0.29186389\n",
            " -0.1417272  -0.11591324]\n",
            "Final cost: 0.0928605507631018\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = sigmoid(np.dot(X_test, theta_final)) >= 0.5\n",
        "accuracy = np.mean(predictions == y_test)\n",
        "print(\"Accuracy:\", accuracy)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oFeWozfmfsUr",
        "outputId": "ef90740a-f10f-4235-ca2d-7584922eeafa"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.985\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Task 4 (45 points): Pick any 2 optimization algorithms that are used to optimize the ‘vanilla’\n",
        "gradient descent. Implement both. You may implement these algorithms yourself OR use a\n",
        "package. In your conclusion, compare both optimization techniques/algorithms with respect to\n",
        "the results you achieve. Also compare these results with the original implementation of\n",
        "gradient descent (Task 3 above). Describe why we should or why we should not use\n",
        "optimization algorithms for the task at hand. For comparison of these algorithms define an\n",
        "evaluation metric appropriate for your problem."
      ],
      "metadata": {
        "id": "wzQGkFFCF5o-"
      }
    }
  ]
}