LinearRegression_(Canada_Per_Capita_Income)(Task-1).ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "L009WtYu6Zdx"
      },
      "outputs": [],
      "source": [
        "#IMPORT INDEPENDENCIES\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N_mSfixA6oYN",
        "outputId": "b8682ff1-f09f-4ff0-f9a0-e80db83ae859"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "#GOOGLE DRIVE MOUNT\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive') "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "CuYPyfLw7G-u"
      },
      "outputs": [],
      "source": [
        "#Loading the dataset\n",
        "dataset = pd.read_csv('/content/drive/MyDrive/Datasets/Datasets/canada_per_capita_income.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N6EfA0cS71RS",
        "outputId": "7cecf485-7063-4d58-94b4-3c3b87ca27f9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "47"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Total Instances\n",
        "len(dataset)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "-QSSJowu77IY"
      },
      "outputs": [],
      "source": [
        "#ASSIGN X & Y VARRIABLES (DEPENDENT & INDEPENDENT)\n",
        "X = dataset.iloc[:,:-1].values\n",
        "y = dataset.iloc[:,-1].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "f9iwuJCj8LOp"
      },
      "outputs": [],
      "source": [
        "#SPLIT THE DATA INTO TRAIN AND TEST DATASETS\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 = 1/2, random_state = 20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "33vLwPRr8NMu",
        "outputId": "56b71044-4ce7-4c8e-ad22-5ba133575768"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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            ],
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        " #TRAIN THE MODEL\n",
        " from sklearn.linear_model import LinearRegression\n",
        " model = LinearRegression()\n",
        " model.fit(X_train, y_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jeFKt9SV9V33",
        "outputId": "3d5c6ce8-9017-4570-f8a1-a2b01430f327"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([ 2044.06405429,  5400.83745168, 29737.44458274, 35611.79802817,\n",
              "        -473.51599375, 10435.99754776, 26380.67118535,  8757.61084907,\n",
              "       31415.83128143, 23023.89778797, -1312.7093431 , 18827.93104123,\n",
              "       19667.12439058, 13792.77094515, 12953.5775958 ,  7079.22415037,\n",
              "        1204.87070495, 30576.63793209,  9596.80419841, 34772.60467882,\n",
              "         365.6773556 , 17988.73769188, 28898.25123339,  2883.25740364])"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#PREDICT THE MODEL\n",
        "prediction = model.predict(X_test)\n",
        "prediction\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "udvRxfl7Iwls",
        "outputId": "fdf31410-3285-46bf-d338-66d9ad108450"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model's accuracy is:  0.9045073513519661\n"
          ]
        }
      ],
      "source": [
        "#Checking the accuracy\n",
        "accuracy = model.score(X_test,y_test)\n",
        "print(\"Model's accuracy is: \", accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "pIxnZRA3AGgT",
        "outputId": "2234eaa5-9ee6-4087-840d-b0096df6da15"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Visualising the Training data results\n",
        "plt.scatter(X_train, y_train, color='r')\n",
        "plt.plot(X_train, y_train , color='b')\n",
        "plt.title('Canada Per Capital Income (Training set)')\n",
        "plt.xlabel('Years')\n",
        "plt.ylabel('Income')\n",
        "plt.plot()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "yz_AEmi8CKlD",
        "outputId": "4cc633b7-55f3-4b53-fb04-6182af9c9f36"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Visualising the FINAL PREDICTION ON TEST DATASET THAT IS NOT TRAINED\n",
        "\n",
        "plt.scatter(X_test, y_test, color='r')\n",
        "plt.plot(X_test, prediction, color='b')\n",
        "plt.title('Canada Per Capital Income (Training set)')\n",
        "plt.xlabel('Years')\n",
        "plt.ylabel('Income')\n",
        "plt.plot()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TIlE9p5GCnW8",
        "outputId": "d6c13942-cb0e-4d70-9e08-0bacd0d6c1c4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R² score:  0.9045073513519661\n"
          ]
        }
      ],
      "source": [
        "#Checking the Accuracy of model\n",
        "from sklearn.metrics import r2_score\n",
        "score = model.score(X_test, y_test)\n",
        "\n",
        "print(\"R² score: \", score)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "FgdxUpUWDS8a"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}