100_Days_of_ML_Code.ipynb

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  "metadata": {
    "colab": {
      "name": "100 Days of ML Code.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gillesvtsilvano/100-Days-Of-ML-Code/blob/master/100_Days_of_ML_Code.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p64dJ1BNoAv0",
        "colab_type": "code",
        "outputId": "383b53b4-6689-4087-f9c0-6a1a4a528d66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "! rm -rf 100-Days-Of-ML-Code && git clone https://github.com/gillesvtsilvano/100-Days-Of-ML-Code"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into '100-Days-Of-ML-Code'...\n",
            "remote: Enumerating objects: 6, done.\u001b[K\n",
            "remote: Counting objects:  16% (1/6)   \u001b[K\rremote: Counting objects:  33% (2/6)   \u001b[K\rremote: Counting objects:  50% (3/6)   \u001b[K\rremote: Counting objects:  66% (4/6)   \u001b[K\rremote: Counting objects:  83% (5/6)   \u001b[K\rremote: Counting objects: 100% (6/6)   \u001b[K\rremote: Counting objects: 100% (6/6), done.\u001b[K\n",
            "remote: Compressing objects: 100% (6/6), done.\u001b[K\n",
            "remote: Total 369 (delta 2), reused 0 (delta 0), pack-reused 363\u001b[K\n",
            "Receiving objects: 100% (369/369), 10.71 MiB | 33.22 MiB/s, done.\n",
            "Resolving deltas: 100% (183/183), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GEN6HQYrodR7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "os.chdir('100-Days-Of-ML-Code')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VYiiUx0tnhEZ",
        "colab_type": "text"
      },
      "source": [
        "# Day 1: Data PreProcessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wLLUiCDDnjwY",
        "colab_type": "text"
      },
      "source": [
        "## Step 1: Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-0yfPjpTnqIY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uL46gyWnnvce",
        "colab_type": "text"
      },
      "source": [
        "## Step 2: Importing dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uAFdctHtnyOH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = pd.read_csv('datasets/Data.csv')\n",
        "\n",
        "# All features but the target variable\n",
        "X = dataset.iloc[:, :-1].values\n",
        "# Only the target variables\n",
        "Y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6VH9EEVZozW7",
        "colab_type": "text"
      },
      "source": [
        "## Step 3: Handling the missing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UNDkFl_7oDg_",
        "colab_type": "code",
        "outputId": "8922a6bd-7da6-4cae-9e04-600e0748b8f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "from sklearn.preprocessing import Imputer\n",
        "\n",
        "imputer = Imputer(missing_values = \"NaN\", strategy = \"mean\", axis = 0)\n",
        "imputer = imputer.fit(X[:, 1:])\n",
        "X[: , 1:3] = imputer.transform(X[:, 1:3])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:66: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
            "  warnings.warn(msg, category=DeprecationWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PAEQzlIOpbgV",
        "colab_type": "text"
      },
      "source": [
        "## Step 4: Encoding categorical data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "80OW9mNGqXpD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "\n",
        "labelencoder_X = LabelEncoder()\n",
        "X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jAKIcWgNq9TC",
        "colab_type": "code",
        "outputId": "65e8e5fd-1338-4a2c-abbb-902e964148d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "ohe = OneHotEncoder(categorical_features = [0])\n",
        "X = ohe.fit_transform(X).toarray()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
            "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
            "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_encoders.py:451: DeprecationWarning: The 'categorical_features' keyword is deprecated in version 0.20 and will be removed in 0.22. You can use the ColumnTransformer instead.\n",
            "  \"use the ColumnTransformer instead.\", DeprecationWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HlpXH5Bjq-Iy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labelencoder_Y = LabelEncoder()\n",
        "Y = labelencoder_Y.fit_transform(Y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ogehHH7prM3h",
        "colab_type": "text"
      },
      "source": [
        "## Step 5: Splitting the datasets into training sets and Test sets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fYJbvtrnsZi3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split( X , Y , test_size = 0.2, random_state = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQr3o2GWtSi9",
        "colab_type": "text"
      },
      "source": [
        "## Step 6: Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nXagB328tmoU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc_X = StandardScaler(with_mean=True)\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_test = sc_X.fit_transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ypApHSX-twd1",
        "colab_type": "code",
        "outputId": "4e68ae81-7f67-4d64-a067-151ed5737b23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "X_test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.,  0.,  0., -1., -1.],\n",
              "       [ 0.,  0.,  0.,  1.,  1.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UWttqEuxtxXE",
        "colab_type": "text"
      },
      "source": [
        "# Day 2: Simple Linear Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QITVyxqOuXBi",
        "colab_type": "text"
      },
      "source": [
        "## Step 1: Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2mneS8tTuhgi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "dataset = pd.read_csv('datasets/studentscores.csv')\n",
        "X = dataset.iloc[:, :-1].values\n",
        "Y = dataset.iloc[:, -1].values\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 1/4, random_state = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p4VbgKupu7gx",
        "colab_type": "text"
      },
      "source": [
        "## Fitting Simple Linear Regression Model to the training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C3UtWR85vHcR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "regressor = LinearRegression()\n",
        "regressor = regressor.fit(X_train, Y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PxKNLTR-vPRR",
        "colab_type": "text"
      },
      "source": [
        "## Step 3: Predicting the Result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UIsF6DX3vTTo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Y_pred = regressor.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_PKMMjlSvWA5",
        "colab_type": "text"
      },
      "source": [
        "## Step 4: Visualising the Training Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WVp4tx-_vbBY",
        "colab_type": "code",
        "outputId": "77b6fc2a-2ecc-48da-fd4b-bced86c5ff86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "plt.scatter(X_train, Y_train, color='red')\n",
        "plt.plot(X_train, regressor.predict(X_train), color='blue')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fa6245354e0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3_kgfepMvo_A",
        "colab_type": "text"
      },
      "source": [
        "## Step 5: Visualising the Test Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2t09AIcwvthA",
        "colab_type": "code",
        "outputId": "66f8ca24-0735-42d1-bdae-14b30b44d603",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "plt.scatter(X_test, Y_test, color='red')\n",
        "plt.plot(X_test, regressor.predict(X_test), color='blue')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fa6244eadd8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e_0W5A8LvzP_",
        "colab_type": "text"
      },
      "source": [
        "# Day 3: Multiple Linear Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q6kSP1WNwR9B",
        "colab_type": "text"
      },
      "source": [
        "## Step 1: Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "35fTyLzpwVmO",
        "colab_type": "code",
        "outputId": "49646747-7845-42cf-cf6c-a144d8cfa19a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "dataset = pd.read_csv('datasets/50_Startups.csv')\n",
        "print(dataset.head())\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   R&D Spend  Administration  Marketing Spend       State     Profit\n",
            "0  165349.20       136897.80        471784.10    New York  192261.83\n",
            "1  162597.70       151377.59        443898.53  California  191792.06\n",
            "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
            "3  144372.41       118671.85        383199.62    New York  182901.99\n",
            "4  142107.34        91391.77        366168.42     Florida  166187.94\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gkqRNj9Iwu-G",
        "colab_type": "code",
        "outputId": "4e9ef7c0-7ec7-45f8-f0e3-81c708275560",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "X = dataset.iloc[:, :-1].values\n",
        "Y = dataset.iloc[:, -1].values\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "\n",
        "labelencoder = LabelEncoder()\n",
        "X[:, 3] = labelencoder.fit_transform(X[: , 3])\n",
        "ohe = OneHotEncoder(categorical_features = [3])\n",
        "X = ohe.fit_transform(X).toarray()[:, 1:] # Avoiding dummy trap"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
            "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
            "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_encoders.py:451: DeprecationWarning: The 'categorical_features' keyword is deprecated in version 0.20 and will be removed in 0.22. You can use the ColumnTransformer instead.\n",
            "  \"use the ColumnTransformer instead.\", DeprecationWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wXIpiILkxj4J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r7IeNRbRxdb1",
        "colab_type": "text"
      },
      "source": [
        "## Step 2: Fitting Multiple Linear Regression to the Training Set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_y3JhQG5xCet",
        "colab_type": "code",
        "outputId": "7d99e56b-14f4-4e7a-c4a7-3afa93d8341e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, Y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3XpgtAVTxta7",
        "colab_type": "text"
      },
      "source": [
        "## Step 3: Predicint the Treining set Result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1-Om8zMvxwPD",
        "colab_type": "code",
        "outputId": "df8cde8c-4afc-4ba8-cf09-0212a37391bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "Y_pred = regressor.predict(X_test)\n",
        "print(regressor.score(X_test,Y_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9347068473282986\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OQ4cJAXVxynz",
        "colab_type": "code",
        "outputId": "0324d858-53c2-427a-91c8-8032e91d9f3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "for test,pred in zip(Y_test,Y_pred):\n",
        "  print(test, pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "103282.38 103015.20159796216\n",
            "144259.4 132582.27760815847\n",
            "146121.95 132447.73845174976\n",
            "77798.83 71976.09851258763\n",
            "191050.39 178537.4822105438\n",
            "105008.31 116161.24230163351\n",
            "81229.06 67851.69209676176\n",
            "97483.56 98791.73374687924\n",
            "110352.25 113969.43533011667\n",
            "166187.94 167921.06569550227\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wjOk4qt70N9H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}