TSP_Task_1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TSP_Task_1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyM4Gi+jVidJFlyz2y93OTAH"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZLxzSRAqYEOl"
      },
      "source": [
        "#**The Sparks Foundation** \n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "##**Data Science and Business Analytics**\n",
        "**TASK 1 - Prediction Using Supervised ML**\n",
        "\n",
        "- Predcit the percentage of an student based on the number of study hours.\n",
        "- Question : What will be the predicted score if a student studies for 9.5 hrs/day? "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nP_iawexYpno"
      },
      "source": [
        "##**Importing required libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FEeKyDRYFW4"
      },
      "source": [
        "# importing the required libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_absolute_error"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lHOGtjNVYyCQ"
      },
      "source": [
        "##**Loading the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TDW62xVZAPz3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "dbf4b616-7bc8-4d28-e2a8-8da70fe49727"
      },
      "source": [
        "data = pd.read_csv('http://bit.ly/w-data')\n",
        "data.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Hours</th>\n",
              "      <th>Scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.5</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.1</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.2</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.5</td>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.5</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Hours  Scores\n",
              "0    2.5      21\n",
              "1    5.1      47\n",
              "2    3.2      27\n",
              "3    8.5      75\n",
              "4    3.5      30"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CVBDyl81fck3"
      },
      "source": [
        "##**Check if there any null value in the Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "fLbViZngY6o4",
        "outputId": "3ba9ee91-8753-4547-b2dc-b1d3be160169"
      },
      "source": [
        "data.isnull==True"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sEqIHFV2ZJ-5"
      },
      "source": [
        "\n",
        "\n",
        "**There is no null value in the Dataset so, we can now visualize our Data.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XOz7YcUucmST"
      },
      "source": [
        "##**Visualizing the Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "_mynUbZdZCHQ",
        "outputId": "ff95c975-24d8-43a0-b539-363c9eec266e"
      },
      "source": [
        "sns.set_style('darkgrid')\n",
        "sns.scatterplot(y= data['Scores'], x= data['Hours'])\n",
        "plt.title('Marks Vs Study Hours',size=20)\n",
        "plt.ylabel('Marks Percentage', size=12)\n",
        "plt.xlabel('Hours Studied', size=12)\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zAaVNlP1ZZsA"
      },
      "source": [
        "**From the above scatter plot there looks to be correlation between the 'Marks Percentage' and 'Hours Studied', Lets plot a regression line to confirm the correlation.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "1EI81YxrZU0r",
        "outputId": "628affb2-68c5-4153-c31b-11206fcc2298"
      },
      "source": [
        "sns.regplot(x= data['Hours'], y= data['Scores'])\n",
        "plt.title('Regression Plot',size=20)\n",
        "plt.ylabel('Marks Percentage', size=12)\n",
        "plt.xlabel('Hours Studied', size=12)\n",
        "plt.show()\n",
        "print(data.corr())"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           Hours    Scores\n",
            "Hours   1.000000  0.976191\n",
            "Scores  0.976191  1.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JF1U74OfZhg5"
      },
      "source": [
        "\n",
        "**It is confirmed that the variables are positively correlated.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xCxspQi6ZoTh"
      },
      "source": [
        "##**Training the model**\n",
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I_BZptJdZwEl"
      },
      "source": [
        "###**1. Splitting the Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HuwoGFbrZdSY"
      },
      "source": [
        "# Defining X and y from the Data\n",
        "X = data.iloc[:, :-1].values  \n",
        "y = data.iloc[:, 1].values\n",
        "\n",
        "# Spliting the Data in two\n",
        "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 0)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-_fw8JM2aAN5"
      },
      "source": [
        "###**2. Fitting the Data into the model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "sPiDDTmCZ7X6",
        "outputId": "9a956e94-252b-4ee2-d006-ee237a312fbb"
      },
      "source": [
        "regression = LinearRegression()\n",
        "regression.fit(train_X, train_y)\n",
        "print(\"---------Model Trained---------\")"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "---------Model Trained---------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i4jYo2vlaWAQ"
      },
      "source": [
        "##**Prediction using percentage of marks**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "aTdaUqAuaO11",
        "outputId": "c4719934-32a2-42c4-80d1-8acfa4391de2"
      },
      "source": [
        "pred_y=regression.predict(val_X)\n",
        "prediction=pd.DataFrame({'Hours': [i[0] for i in val_X],'Predicted Marks':[k for k in pred_y]})\n",
        "prediction"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Hours</th>\n",
              "      <th>Predicted Marks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.5</td>\n",
              "      <td>16.844722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.2</td>\n",
              "      <td>33.745575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.4</td>\n",
              "      <td>75.500624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.5</td>\n",
              "      <td>26.786400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.9</td>\n",
              "      <td>60.588106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.8</td>\n",
              "      <td>39.710582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.9</td>\n",
              "      <td>20.821393</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Hours  Predicted Marks\n",
              "0    1.5        16.844722\n",
              "1    3.2        33.745575\n",
              "2    7.4        75.500624\n",
              "3    2.5        26.786400\n",
              "4    5.9        60.588106\n",
              "5    3.8        39.710582\n",
              "6    1.9        20.821393"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jmSVlmrEazE5"
      },
      "source": [
        "##**Comparing the predicted marks with the actual marks**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "LubF4H0MavtC",
        "outputId": "6ec96036-004d-4547-92bc-0d48f8f4d2b1"
      },
      "source": [
        "compare_scores=pd.DataFrame({'Actual Marks': val_y, 'Predicted Marks': pred_y})\n",
        "compare_scores"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Actual Marks</th>\n",
              "      <th>Predicted Marks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20</td>\n",
              "      <td>16.844722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>27</td>\n",
              "      <td>33.745575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>69</td>\n",
              "      <td>75.500624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30</td>\n",
              "      <td>26.786400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>62</td>\n",
              "      <td>60.588106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>35</td>\n",
              "      <td>39.710582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>24</td>\n",
              "      <td>20.821393</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Actual Marks  Predicted Marks\n",
              "0            20        16.844722\n",
              "1            27        33.745575\n",
              "2            69        75.500624\n",
              "3            30        26.786400\n",
              "4            62        60.588106\n",
              "5            35        39.710582\n",
              "6            24        20.821393"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hEGjoLocbIVV"
      },
      "source": [
        "##**Comparing the predicted marks with the actual marks visually**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "ILa-G0tja_2Q",
        "outputId": "047d4461-d438-4eb1-909e-f267882a4bf0"
      },
      "source": [
        "plt.scatter(x=val_X, y=val_y, color='blue')\n",
        "plt.plot(val_X, pred_y, color='Black')\n",
        "plt.title('Actual vs Predicted', size=20)\n",
        "plt.ylabel('Marks Percentage', size=12)\n",
        "plt.xlabel('Hours Studied', size=12)\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Erk_Xyl8bY2Y"
      },
      "source": [
        "##**Evaluating the model**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "j7E0BpaNbRnh",
        "outputId": "18e55958-a551-4256-d58d-4db2db39d12b"
      },
      "source": [
        "#Calculating the accuracy of the model\n",
        "print('Mean absolute error: ',mean_absolute_error(val_y,pred_y))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean absolute error:  4.130879918502482\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8WuvB2y7bmQ5"
      },
      "source": [
        "\n",
        "##**Q: What will be the predicted score of a student if he/she studies for 9.25 hrs/ day?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Dlwa4GsWbeKk",
        "outputId": "2a4691ae-e34b-41f1-baf9-9829ac3e50dd"
      },
      "source": [
        "hours = [9.25]\n",
        "answer = regression.predict([hours])\n",
        "print(\"According to the regression model if a student studies for 9.25 hours a day he/she is likely to score {}\".format(round(answer[0],3))+\" marks.\")"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "According to the regression model if a student studies for 9.25 hours a day he/she is likely to score 93.893 marks.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bsRUEgkNq0oQ"
      },
      "source": [
        "#**Thank You!!!**"
      ]
    }
  ]
}