TSF GRIP Task1 Solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "abf5f2d7",
   "metadata": {},
   "source": [
    "## **Linear Regression with Python Scikit Learn**\n",
    "In this section we will see how the Python Scikit-Learn library for machine learning can be used to implement regression functions. We will start with simple linear regression involving two variables.\n",
    "\n",
    "### **Simple Linear Regression**\n",
    "In this regression task we will predict the percentage of marks that a student is expected to score based upon the number of hours they studied. This is a simple linear regression task as it involves just two variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8882491c",
   "metadata": {},
   "source": [
    "## Author : Saragadam Abhiram Laxmi Raj\n",
    "## Task 1  : Prediction using Supervised Machine Learning\n",
    "## GRIP @ The Sparks Foundation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f944390a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the required libraries\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2d4bdfe",
   "metadata": {},
   "source": [
    "###  Step 1 - Reading the data from source\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "be744576",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data imported successfully\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.2</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.5</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.3</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.7</td>\n",
       "      <td>25</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\n",
       "5    1.5      20\n",
       "6    9.2      88\n",
       "7    5.5      60\n",
       "8    8.3      81\n",
       "9    2.7      25"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading data from remote link\n",
    "url = \"http://bit.ly/w-data\"\n",
    "s_data = pd.read_csv(url)\n",
    "print(\"Data imported successfully\")\n",
    "\n",
    "s_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "739ca4d5",
   "metadata": {},
   "source": [
    "###  Step 2 - Input data Visualization\n",
    "\n",
    "#### Let's plot our data points on 2-D graph to eyeball our dataset and see if we can manually find any relationship between the data. We can create the plot with the following script:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1f1d5523",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the distribution of scores\n",
    "s_data.plot(x='Hours', y='Scores', style='o')  \n",
    "plt.title('Hours vs Percentage')  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4e1c7ac",
   "metadata": {},
   "source": [
    "##### From the graph above, we can clearly see that there is a positive linear relation between the number of hours studied and percentage of score."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "257329f4",
   "metadata": {},
   "source": [
    "###  Step 3- Data Processing (Preparing the data)\n",
    "\n",
    "The next step is to divide the data into \"attributes\" (inputs) and \"labels\" (outputs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6e78ce22",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = s_data.iloc[:, :-1].values  \n",
    "y = s_data.iloc[:, 1].values  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71256874",
   "metadata": {},
   "source": [
    "#### Now that we have our attributes and labels, the next step is to split this data into training and test sets. We'll do this by using Scikit-Learn's built-in train_test_split() method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e4fcb108",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split  \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                            test_size=0.2, random_state=0) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a91cfb6a",
   "metadata": {},
   "source": [
    "### Step 4 -Training the Algorithm\n",
    "We have split our data into training and testing sets, and now is finally the time to train our algorithm. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0148bdeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression  \n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train) \n",
    "\n",
    "print(\"Training complete.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea5f8d0f",
   "metadata": {},
   "source": [
    "###  Step 5 - Plotting the Line of regression\n",
    "Now since our model is trained now, its the time to visualize the best-fit line of regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "acee24cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the regression line\n",
    "line = regressor.coef_*X+regressor.intercept_\n",
    "\n",
    "# Plotting for the test data\n",
    "plt.scatter(X, y)\n",
    "plt.plot(X, line,color='red');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e78b675",
   "metadata": {},
   "source": [
    "###  Step 6 - Making Predictions\n",
    "Now that we have trained our algorithm, it's time to make some predictions.\n",
    "\n",
    "For this we will use our test-set data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dd3aaa19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.5]\n",
      " [3.2]\n",
      " [7.4]\n",
      " [2.5]\n",
      " [5.9]]\n"
     ]
    }
   ],
   "source": [
    "# Testing data\n",
    "print(X_test)\n",
    "# Model Prediction \n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dab4acc1",
   "metadata": {},
   "source": [
    " ### Step 7 - Comparing Actual result to the Predicted Model result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ac8ca54e",
   "metadata": {},
   "outputs": [
    {
     "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</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>16.884145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>33.732261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>75.357018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>26.794801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>60.491033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  16.884145\n",
       "1      27  33.732261\n",
       "2      69  75.357018\n",
       "3      30  26.794801\n",
       "4      62  60.491033"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing Actual vs Predicted\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred}) \n",
    "df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "35d84362",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours = 9.25\n",
      "Predicted Score = 93.69173248737538\n"
     ]
    }
   ],
   "source": [
    "# Solution to given question\n",
    "hours = 9.25\n",
    "own_pred = regressor.predict([[hours]])\n",
    "print(\"No of Hours = {}\".format(hours))\n",
    "print(\"Predicted Score = {}\".format(own_pred[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc450153",
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   "source": [
    "### Step8 -  **Evaluating the model**\n",
    "\n",
    "The final step is to evaluate the performance of algorithm. This step is particularly important to compare how well different algorithms perform on a particular dataset. For simplicity here, we have chosen the mean square error. There are many such metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2e13eed8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.183859899002975\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:', \n",
    "      metrics.mean_absolute_error(y_test, y_pred)) "
   ]
  }
 ],
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