TASK-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt text](https://i.ibb.co/88MhmQL/Slide1.jpg \"Logo Title Text 1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Author : Rishabh Chaturvedi \n",
    "[![alt text](https://logoeps.com/wp-content/uploads/2014/02/25231-github-cat-in-a-circle-icon-vector-icon-vector-eps.png \"Git Hub Link\")](https://github.com/devrc1234)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem Statement: Predict the perecentage of the student based on the no. of hours student studied using Simple Linear Regression model which involves two variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Importing the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# warnings \n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error as MAE\n",
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Understanding the DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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      ],
      "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"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the dataset\n",
    "url=\"http://bit.ly/w-data\"\n",
    "df=pd.read_csv(url)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 2)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>count</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.012000</td>\n",
       "      <td>51.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.525094</td>\n",
       "      <td>25.286887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.700000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours     Scores\n",
       "count  25.000000  25.000000\n",
       "mean    5.012000  51.480000\n",
       "std     2.525094  25.286887\n",
       "min     1.100000  17.000000\n",
       "25%     2.700000  30.000000\n",
       "50%     4.800000  47.000000\n",
       "75%     7.400000  75.000000\n",
       "max     9.200000  95.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25 entries, 0 to 24\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Hours   25 non-null     float64\n",
      " 1   Scores  25 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 528.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating for Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "      <th>0</th>\n",
       "      <td>False</td>\n",
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       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hours  Scores\n",
       "0   False   False\n",
       "1   False   False\n",
       "2   False   False\n",
       "3   False   False\n",
       "4   False   False\n",
       "5   False   False\n",
       "6   False   False\n",
       "7   False   False\n",
       "8   False   False\n",
       "9   False   False\n",
       "10  False   False\n",
       "11  False   False\n",
       "12  False   False\n",
       "13  False   False\n",
       "14  False   False\n",
       "15  False   False\n",
       "16  False   False\n",
       "17  False   False\n",
       "18  False   False\n",
       "19  False   False\n",
       "20  False   False\n",
       "21  False   False\n",
       "22  False   False\n",
       "23  False   False\n",
       "24  False   False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hours     float64\n",
       "Scores      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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 distribution of scores to unserstand the relation between the data.\n",
    "df.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": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='hist', figsize=(8, 5))\n",
    "plt.title('Hours vs Percentage')  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='bar',figsize=(8,5))\n",
    "plt.title('Hours vs Percentage')  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the correlations\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.heatmap(df.corr(),annot=True,cmap=\"YlGnBu\",annot_kws={'fontsize':10})\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing Data for Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, :-1].values  \n",
    "y = df.iloc[:, 1].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data into test and train\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.25, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training is complete.\n"
     ]
    }
   ],
   "source": [
    "# Building the linear model and train it\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train,y_train)\n",
    "print('Training is complete.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The coefficient is:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([9.94167834])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"The coefficient is:\")\n",
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the best fitted line \n",
    "line = lr.intercept_+lr.coef_*X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the best fitted line on the graph \n",
    "plt.figure(figsize=(10,5))\n",
    "sns.scatterplot(df['Hours'],df['Scores'],s=200,color='r')\n",
    "plt.plot(df['Hours'],line,'b')\n",
    "plt.xlabel(xlabel='Hours',fontsize=20)\n",
    "plt.ylabel(ylabel='Scores',fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.5]\n",
      " [3.2]\n",
      " [7.4]\n",
      " [2.5]\n",
      " [5.9]\n",
      " [3.8]\n",
      " [1.9]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test) # Testing data - In Hours\n",
    "y_pred = lr.predict(X_test) # Predicting the scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>16.844722</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  Predicted\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"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing Actual vs Predicted values\n",
    "datafr = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
    "datafr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours = 9.25\n",
      "Predicted Score = 93.89272889341655\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Testting the model to redict the percentage of student if he studies for 9.25 hours as given\n",
    "hours = 9.25\n",
    "pred = lr.predict(np.array(hours).reshape(-1,1))\n",
    "print(\"No of Hours = {}\".format(hours))\n",
    "print(\"Predicted Score = {}\".format(pred[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Evaluating the performance of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.130879918502486\n",
      "Mean Squared Error: 20.33292367497997\n",
      "Root Mean Squared Error: 4.5092043283688055\n"
     ]
    }
   ],
   "source": [
    "#The final step is to evaluate the performance of the algorithm. \n",
    "#This step is particularly important to compare how well different algorithms perform on a particular dataset\n",
    "from sklearn import metrics  \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",
   "metadata": {},
   "source": [
    "### Conclusion: We can see the value of the root mean squared error is 4.509 and the mean value of the percentages of all the students is 51.48 (from the statistical details obtained in cell no. 5). Here root mean squared error is less than 10% of the mean value of the percenttages of all the student scores. Hence, it is safe to conclue the model did a decent job to predict the the student score as 93.89 % when student studies for 9.25 hours."
   ]
  }
 ],
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