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20_21-J09
Commit b459ec42 authored by IT17176798_Bhagya Dilhara's avatar IT17176798_Bhagya Dilhara
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'updated_python_codes'

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marks.prediction.ipynb
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......@@ -2,313 +2,105 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
"%matplotlib inline\n",
"import pandas as pd\n",
"import mysql.connector\n",
"mydb = mysql.connector.connect(\n",
" host=\"localhost\",\n",
" user=\"root\",\n",
" password=\"root\",\n",
" database=\"inteljr\"\n",
")\n",
"\n",
"student_id = 1\n",
"\n",
"mycursor = mydb.cursor()\n",
"sql = \"\"\"SELECT duration, marks FROM tbl_result_summary WHERE student_id = '%s' order by added_date desc\"\"\" % (student_id) \n",
"mycursor.execute(sql)\n",
"myresult = mycursor.fetchall()\n",
"df=pd.DataFrame(myresult)\n",
"df.shape\n",
"df.head()\n",
"df.describe()\n",
"\n",
"\n",
"df = pd.read_sql_query(sql,mydb)\n",
"df.plot(x='duration',y='marks',style='*')\n",
"plt.title('Student Mark Prediction')\n",
"plt.xlabel('Hours')\n",
"plt.ylabel('Marks')\n",
"plt.show()\n",
"\n",
"x = df.iloc[:, :-1].values\n",
"y = df.iloc[:, 1].values\n",
"\n",
"#Split the data into train and test dataset\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=0.5,random_state=0)\n",
"\n",
"#Fitting Simple Linear regression data model to train data set\n",
"from sklearn.linear_model import LinearRegression\n",
"regressorObject=LinearRegression()\n",
"pd.plotting.register_matplotlib_converters()\n",
"regressorObject.fit(x_train,y_train)\n",
"\n",
"#predict the test set\n",
"y_pred_test_data=regressorObject.predict(x_test)\n",
"y_pred_train_data=regressorObject.predict(x_train)\n",
"\n",
"# Visualising the Training set results in a scatter plot\n",
"plt.scatter(x_train, y_train, color = 'red')\n",
"plt.plot(x_train, regressorObject.predict(x_train), color = 'blue')\n",
"plt.xlabel('Duration ')\n",
"plt.ylabel('Marks')\n",
"plt.show()\n",
"\n",
"print(regressorObject.intercept_)\n",
"print(regressorObject.coef_)\n",
"df=pd.DataFrame({'actual':y_pred_train_data, 'predicted':y_pred_test_data})\n",
"df\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset = pd.read_csv(\"D:/rashmika/projects/freelance/eLearning-analysis/dataset.csv\") "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(25, 2)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"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>hours</th>\n",
" <th>score</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 score\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": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"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>hours</th>\n",
" <th>score</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 score\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": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"dataset.describe()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.0181600414346974\n",
"[9.91065648]\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>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": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"dataset.plot(x='hours',y='score',style='*')\n",
"plt.title('Student Mark Prediction')\n",
......@@ -331,13 +123,6 @@
"df=pd.DataFrame({'Actual':Y_test, 'Predicted':Y_pred})\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
......@@ -361,4 +146,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
}
}
\ No newline at end of file
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