Task 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c40c1cff",
   "metadata": {},
   "source": [
    " # AUTHOR : RAJIL JAIN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c0020eb",
   "metadata": {},
   "source": [
    "# GRIP@The Sparks Foundation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37c96ca0",
   "metadata": {},
   "source": [
    "## Task-1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61a1ea38",
   "metadata": {},
   "source": [
    "# Prediction using Supervised Machine Learning "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1611503c",
   "metadata": {},
   "source": [
    "In this regression task I tried to predict the percentage of marks that a student is expected to score based upon the number of hours they studied.\n",
    "This is a simple regression task as it involves just two variables.\n",
    "\n",
    "Language:Python\n",
    "\n",
    "IDE:Jupyter Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bf1c0d75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing all Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7c16df6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data imported successfully\n"
     ]
    },
    {
     "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",
       "    <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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading data from remote link\n",
    "url = \"http://Bit.ly/w-data\"\n",
    "s=pd.read_csv(url)\n",
    "print(\"Data imported successfully\")\n",
    "s.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81919018",
   "metadata": {},
   "source": [
    "Let's plot our points on 2D graph to eyeball our dataset and see if we can manually find any relationship between the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f7d32521",
   "metadata": {
    "scrolled": true
   },
   "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 distribution of scores \n",
    "s.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": "68a03e9e",
   "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": "6c8b6b63",
   "metadata": {},
   "source": [
    "## Preparing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd932911",
   "metadata": {},
   "source": [
    "The next step is to divide the data into \"attributes\" (input) and \"labels\" (outputs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d547e6c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = s.iloc[:, :-1].values\n",
    "y = s.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a464bb2f",
   "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": 113,
   "id": "d7bee5b8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "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)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deead564",
   "metadata": {},
   "source": [
    "## Training the Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24b361e5",
   "metadata": {},
   "source": [
    "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": 108,
   "id": "30b5a107",
   "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.reshape(-1,1), y_train)\n",
    "\n",
    "print(\"Training complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "34858faa",
   "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)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7774e276",
   "metadata": {},
   "source": [
    "## Making Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edaf805c",
   "metadata": {},
   "source": [
    "Now that we have trained our algorithm, its time to make some predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b1a17f96",
   "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 - In Hours\n",
    "print(X_test)\n",
    "# Predicting the scores\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5ecd78ab",
   "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": 88,
     "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": 105,
   "id": "715aca25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting difference\n",
    "df.plot(kind='line', figsize=(5,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "4ff392a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours = 9.25\n",
      "Predicted Score = 93.69173248737535\n"
     ]
    }
   ],
   "source": [
    "# Testing the model with our own desired data\n",
    "hours = 9.25\n",
    "test=np.array([hours])\n",
    "test=test.reshape(-1,1)\n",
    "t_pred = regressor.predict(test)\n",
    "print(\"No of Hours = {}\".format(hours))\n",
    "print(\"Predicted Score = {}\".format(t_pred[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "020db896",
   "metadata": {},
   "source": [
    "## Evaluating the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60bc2449",
   "metadata": {},
   "source": [
    "The final step is to evaluate the performance of algorithm. This step is particulary important to compare how well different algorithms perfom on a particular dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "da36e4b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.183859899002975\n",
      "Mean Squared Error: 21.598769307217406\n",
      "Root Mean Squared Error: 4.647447612100367\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "print(\"Mean Absolute Error:\", metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:',metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:',np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c566aa54",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c257e32e",
   "metadata": {},
   "source": [
    "Thats how i was successfully able to make out prediction using supervised ML and performed various analysis using model. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "998dea26",
   "metadata": {},
   "source": [
    "Thank You"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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}