Untitled0.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled0.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1dVgGDMVdC3l_9M5QSsFbvItEjFGj58ai",
      "authorship_tag": "ABX9TyN33VmoM//RIdnoyNGae5lE",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/UNCANNY69/CODE_SEM4_DAA/blob/master/Untitled0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "import libraries"
      ],
      "metadata": {
        "id": "yIeagAykVvJm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "cdMPKllvOzQ9"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "import dataset"
      ],
      "metadata": {
        "id": "vBc3lFZ6WBLF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/MACHINE_LEARNING_SOURCE _CODES/Part 1 - Data Preprocessing/Section 2 -------------------- Part 1 - Data Preprocessing --------------------/Python/Data.csv\")\n",
        "x = dataset.iloc[:,:-1].values\n",
        "y = dataset.iloc[:,-1:].values"
      ],
      "metadata": {
        "id": "npo5O5FKWAqH"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Filling missing Data"
      ],
      "metadata": {
        "id": "PNBCCL_s95sX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "impute = SimpleImputer(missing_values = np.nan , strategy = 'mean')\n",
        "impute.fit(x[:,1:3])\n",
        "x[:,1:3] = impute.transform(x[:,1:3])"
      ],
      "metadata": {
        "id": "Lab_IIzi955f"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Encoding Data"
      ],
      "metadata": {
        "id": "LkympdJHX8LU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "colt1 = ColumnTransformer(transformers=[('encoder' , OneHotEncoder() , [0])] , remainder='passthrough')\n",
        "x = np.array(colt1.fit_transform(x))"
      ],
      "metadata": {
        "id": "Axnxv5E-X8YH"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Encoding dependent Data"
      ],
      "metadata": {
        "id": "Ipnn8Xx6dtuo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IKq-aiKmdtaK",
        "outputId": "505f9363-d894-44b9-a5d6-04ec3ec879bd"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:115: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Trainning and Test split "
      ],
      "metadata": {
        "id": "rDJFu1Ilh5D7"
      }
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "QAvnGd-Dh5P7"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feature scaling"
      ],
      "metadata": {
        "id": "9Xjz-2cGV9u8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "x_train[:,3:] = sc.fit_transform(x_train[:,3:])\n",
        "x_test[:,3:] = sc.transform(x_test[:,3:])\n"
      ],
      "metadata": {
        "id": "bhp-xbL_V72L"
      },
      "execution_count": 32,
      "outputs": []
    }
  ]
}