{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#6204641515"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exam Instruction EE522 Fall 2022:\n",
" \n",
"1. Answer all of the following questions. Each answer should be thorough, complete, and relevant. Points will be deducted for irrelevant details. Use the back of the pages if you need more room for your answer.\n",
"\n",
"2. The points are a clue about how much time you should spend on each question. Plan your time accordingly.\n",
"\n",
"3. There are 3 questions. The total score is 100 points accounted for 25 percent of the total scores.\n",
"\n",
"4. Exam Date and time: October 1, 2022 from 1-4 pm. \n",
"\n",
"\n",
"5. You have to submit (1) the Jupiter Notebook code (.ipynp) with the name: your student id.ipynp i.e 6504610069.ipynp \n",
"\n",
"6. You have to submit your code on BE-moodle.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question 1 [50 points]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Set Information:\n",
"\n",
"Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues. \n",
"\n",
"Apart from interesting real world applications of bike sharing systems, the characteristics of data being generated by these systems make them attractive for the research. Opposed to other transport services such as bus or subway, the duration of travel, departure and arrival position is explicitly recorded in these systems. This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city. Hence, it is expected that most of important events in the city could be detected via monitoring these data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dataset characteristics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\t\n",
"=========================================\n",
"Dataset characteristics\n",
"======================\t\n",
"hour.csv has the following fields:\n",
"\t\n",
"\t- instant: record index\n",
"\t- dteday : date\n",
"\t- season : season (1:springer, 2:summer, 3:fall, 4:winter)\n",
"\t- yr : year (0: 2011, 1:2012)\n",
"\t- mnth : month ( 1 to 12)\n",
"\t- hr : hour (0 to 23)\n",
"\t- holiday : weather day is holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule)\n",
"\t- weekday : day of the week\n",
"\t- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.\n",
"\t+ weathersit : \n",
"\t\t- 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
"\t\t- 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
"\t\t- 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
"\t\t- 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
"\t- temp : Normalized temperature in Celsius. The values are divided to 41 (max)\n",
"\t- atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max)\n",
"\t- hum: Normalized humidity. The values are divided to 100 (max)\n",
"\t- windspeed: Normalized wind speed. The values are divided to 67 (max)\n",
"\t- casual: count of casual users\n",
"\t- registered: count of registered users\n",
"\t- cnt: count of total rental bikes including both casual and registered"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question 1.1 :"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the scatter plots to visualize the relationship between temp and target values (cnt). Then, plot the heatmap to represent the correlation matrix between features and target values.\n",
"\n",
"Also, calculate the median of cout of total bikes (cnt) separated by weathersit, season ,and holiday. Is there any interesting thing you found? Explain carefully.\n",
"\n",
"\n",
"[20 Points]\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
" cnt\n",
"weathersit \n",
"1 159.0\n",
"2 133.0\n",
"3 63.0\n",
"4 36.0"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# median of count of total bike seperated by weathersit\n",
"df.groupby(by=['weathersit'])[['cnt']].median()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From this, it can be observed that the rental bike is more popular when the weather is 1. Clear or 2. Mist+cloudy as the median is significantly higher than other group. Also, it can be observed that the rental bike is the least popular when the weather is a heavy rain. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"\n",
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155.5
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"
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],
"text/plain": [
" cnt\n",
"season \n",
"1 76.0\n",
"2 165.0\n",
"3 199.0\n",
"4 155.5"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# median of count of total bike seperated by season\n",
"df.groupby(by=['season'])[['cnt']].median()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It can be observed that the most popular season for the users to use rental bike is fall, summer, winter, and spring respectively."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
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"\n",
"
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" \n",
"
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"
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],
"text/plain": [
" cnt\n",
"holiday \n",
"0 144.0\n",
"1 97.0"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# median of count of total bike seperated by holiday\n",
"df.groupby(by=['holiday'])[['cnt']].median()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the median seperate by holiday, it can be observed that the rental bikes are being used more when it is not a holiday."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question 1.2 :"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will start by training our data. Please split the dataset into 80 % train data and 20 % test data. Then, use two features which are atemp and weatherlist to train the Regression algorithm and make predictions. What is the MAE and RMSE of train and test data in this model? Interpret the coefficients of these tow variables carefully.[10 Points]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"#Here is the space for your codes\n",
"X = df[['atemp','weathersit']]\n",
"y = df[['cnt']]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_22108\\1024352887.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" X['clear'] = X['weathersit'].map({1:'Yes', 2:'No', 3:'No', 4:'No'})\n",
"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_22108\\1024352887.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" X['mist'] = X['weathersit'].map({1:'No', 2:'Yes', 3:'No', 4:'No'})\n",
"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_22108\\1024352887.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" X['light_snow'] = X['weathersit'].map({1:'No', 2:'No', 3:'Yes', 4:'No'})\n",
"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_22108\\1024352887.py:5: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" X['heavy_rain'] = X['weathersit'].map({1:'No', 2:'No', 3:'No', 4:'Yes'})\n"
]
}
],
"source": [
"# make weathersit a dummy variable\n",
"X['clear'] = X['weathersit'].map({1:'Yes', 2:'No', 3:'No', 4:'No'})\n",
"X['mist'] = X['weathersit'].map({1:'No', 2:'Yes', 3:'No', 4:'No'})\n",
"X['light_snow'] = X['weathersit'].map({1:'No', 2:'No', 3:'Yes', 4:'No'})\n",
"X['heavy_rain'] = X['weathersit'].map({1:'No', 2:'No', 3:'No', 4:'Yes'})"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_22108\\2964721994.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" X[\"clear\"] = X[\"clear\"].map({'Yes':1, 'No':0})\n",
"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_22108\\2964721994.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" X[\"mist\"] = X[\"mist\"].map({'Yes':1, 'No':0})\n",
"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_22108\\2964721994.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" X[\"light_snow\"] = X[\"light_snow\"].map({'Yes':1, 'No':0})\n",
"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_22108\\2964721994.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" X[\"heavy_rain\"] = X[\"heavy_rain\"].map({'Yes':1, 'No':0})\n"
]
}
],
"source": [
"X[\"clear\"] = X[\"clear\"].map({'Yes':1, 'No':0})\n",
"X[\"mist\"] = X[\"mist\"].map({'Yes':1, 'No':0})\n",
"X[\"light_snow\"] = X[\"light_snow\"].map({'Yes':1, 'No':0})\n",
"X[\"heavy_rain\"] = X[\"heavy_rain\"].map({'Yes':1, 'No':0})"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"X = X.drop(columns=\"weathersit\")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"X = X.drop(columns=\"clear\")\n",
"# drop one dummy variable to avoid perfect collinearity"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import (linear_model, metrics, model_selection)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fit model: count of rental bikes = 1.1073800211519824 + 415.7300836812466 atemp + -15.238512841897247mist +-67.95810160616331light snow + -4.4739993278439965 heavy rain\n",
"126.5171643868986 MAE test\n",
"124.09181461368966 MAE train\n",
"167.05219292363986 RMSE test\n",
"164.48952274402754 RMSE train\n"
]
}
],
"source": [
"cnt_weather_model = linear_model.LinearRegression()\n",
"\n",
"X_train,X_test,y_train,y_test = model_selection.train_test_split(X,y, test_size=0.2) # % of test size to total data\n",
"model1 = cnt_weather_model.fit(X_train,y_train)\n",
"\n",
"beta_0 = cnt_weather_model.intercept_[0]\n",
"beta_1 = cnt_weather_model.coef_[0][0]\n",
"beta_2 = cnt_weather_model.coef_[0][1]\n",
"beta_3 = cnt_weather_model.coef_[0][2]\n",
"beta_4 = cnt_weather_model.coef_[0][3]\n",
"\n",
"print(f\"Fit model: count of rental bikes = {beta_0} + {beta_1} atemp + {beta_2}mist +{beta_3}light snow + {beta_4} heavy rain\")\n",
"\n",
"\n",
"MAE_test = metrics.mean_absolute_error(y_test, model1.predict(X_test))\n",
"MAE_train = metrics.mean_absolute_error(y_train, model1.predict(X_train))\n",
"\n",
"RMSE_test = np.sqrt(metrics.mean_squared_error(y_test, model1.predict(X_test)))\n",
"RMSE_train = np.sqrt(metrics.mean_squared_error(y_train, model1.predict(X_train)))\n",
"\n",
"print(MAE_test,\"MAE test\")\n",
"print(MAE_train, \"MAE train\")\n",
"print(RMSE_test,\"RMSE test\")\n",
"print(RMSE_train,\"RMSE train\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Beta_atemp = 415.73 means that, when the normalized temperature increases by 1, the predicted rental bikes will increase by 414.73.\n",
"\n",
"Beta_mist = -15.24 means that if the weather is mist+clound, the predicted decrease in rental bike is 15.24.\n",
"\n",
"Beta_light snow = -67.96 means that if it has light snow, the predicted decrease in rental bike is 67.96.\n",
"\n",
"Beta_heavy rain = -4.47 means that if it rains heavily, the predicted decrease in rental bike is 4.47.\n",
"\n",
"If the weather is clear, then the model is \"count of rental bikes = 1.1073800211519824 + 415.7300836812466 atemp\"."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question 1.3 :"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Perform the cross-varidation by spliting the dataset into 10 folds and train the model with Regression algorithm by using the two features as in question 1.2. What is the MAE and RMSE in model?.[10 Points]"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"#Here is the space for your codes\n",
"from sklearn.model_selection import KFold\n",
"kf = KFold(n_splits=10,shuffle=True)\n",
"X=X.to_numpy()\n",
"y=y.to_numpy()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RMSE on 10-fold CV on the test: 164.97779116120992\n",
"RMSE on 10-fold CV on the train: 164.99888207870436\n"
]
}
],
"source": [
"err_train = 0\n",
"err_test =0\n",
"for train,test in kf.split(X):\n",
" lr=linear_model.LinearRegression()\n",
" reg=lr.fit(X[train],y[train])\n",
" y_pred_train =reg.predict(X[train])\n",
" y_pred_test =reg.predict(X[test])\n",
" e_train= y[train]-y_pred_train\n",
" e_test = y[test]-y_pred_test\n",
" err_test += np.sqrt(np.mean(e_test*e_test)) \n",
" err_train += np.sqrt(np.mean(e_train*e_train)) \n",
"rmse_10cv_test = err_test/10 \n",
"rmse_10cv_train = err_train/10 \n",
"print('RMSE on 10-fold CV on the test: {}'.format(rmse_10cv_test))\n",
"print('RMSE on 10-fold CV on the train: {}'.format(rmse_10cv_train))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MAE on 10-fold CV on the train data: 124.63869051393392\n",
"MAE on 10-fold CV on the test data: 124.60255876634714\n"
]
}
],
"source": [
"err_train = 0\n",
"err_test =0\n",
"for train,test in kf.split(X):\n",
" lr=linear_model.LinearRegression()\n",
" reg=lr.fit(X[train],y[train])\n",
" y_pred_train =reg.predict(X[train])\n",
" y_pred_test =reg.predict(X[test])\n",
" e_train= y[train]-y_pred_train\n",
" e_test = y[test]-y_pred_test\n",
" err_train += metrics.mean_absolute_error(y[train], reg.predict(X[train])) \n",
" err_test += metrics.mean_absolute_error(y[test], reg.predict(X[test]))\n",
"mae_10cv_test = err_train/10 \n",
"mae_10cv_train = err_test/10 \n",
"print('MAE on 10-fold CV on the train data: {}'.format(mae_10cv_train))\n",
"print('MAE on 10-fold CV on the test data: {}'.format(mae_10cv_test))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The last excercise, using all features which are\n",
"- holiday : weather day is holiday or not \n",
"- weekday : day of the week\n",
"- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.\n",
"+ weathersit : \n",
" - 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
" - 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
" - 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
" - 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
"- temp : Normalized temperature in Celsius. The values are divided to 41 (max)\n",
"- atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max)\n",
"- hum: Normalized humidity. The values are divided to 100 (max)\n",
"- windspeed: Normalized wind speed. The values are divided to 67 (max)\n",
"\n",
"to train the model with the same process in question 1.2 What is the MAE and RMSE in this model? Interpret the results carefully"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [],
"source": [
"#Here is the space for your codes\n",
"All = df[['holiday','weekday','workingday','weathersit','temp','atemp','hum','windspeed']]\n",
"y = df[['cnt']]"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_22108\\1008955906.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" All['clear'] = All['weathersit'].map({1:'Yes', 2:'No', 3:'No', 4:'No'})\n",
"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_22108\\1008955906.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" All['mist'] = All['weathersit'].map({1:'No', 2:'Yes', 3:'No', 4:'No'})\n",
"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_22108\\1008955906.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" All['light_snow'] = All['weathersit'].map({1:'No', 2:'No', 3:'Yes', 4:'No'})\n",
"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_22108\\1008955906.py:5: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" All['heavy_rain'] = All['weathersit'].map({1:'No', 2:'No', 3:'No', 4:'Yes'})\n"
]
}
],
"source": [
"# make weathersit a dummy variable\n",
"All['clear'] = All['weathersit'].map({1:'Yes', 2:'No', 3:'No', 4:'No'})\n",
"All['mist'] = All['weathersit'].map({1:'No', 2:'Yes', 3:'No', 4:'No'})\n",
"All['light_snow'] = All['weathersit'].map({1:'No', 2:'No', 3:'Yes', 4:'No'})\n",
"All['heavy_rain'] = All['weathersit'].map({1:'No', 2:'No', 3:'No', 4:'Yes'})"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_22108\\1788574603.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" All[\"clear\"] = All[\"clear\"].map({'Yes':1, 'No':0})\n",
"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_22108\\1788574603.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" All[\"mist\"] = All[\"mist\"].map({'Yes':1, 'No':0})\n",
"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_22108\\1788574603.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" All[\"light_snow\"] = All[\"light_snow\"].map({'Yes':1, 'No':0})\n",
"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_22108\\1788574603.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" All[\"heavy_rain\"] = All[\"heavy_rain\"].map({'Yes':1, 'No':0})\n"
]
}
],
"source": [
"All[\"clear\"] = All[\"clear\"].map({'Yes':1, 'No':0})\n",
"All[\"mist\"] = All[\"mist\"].map({'Yes':1, 'No':0})\n",
"All[\"light_snow\"] = All[\"light_snow\"].map({'Yes':1, 'No':0})\n",
"All[\"heavy_rain\"] = All[\"heavy_rain\"].map({'Yes':1, 'No':0})"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {},
"outputs": [],
"source": [
"All = All.drop(columns=\"weathersit\")"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {},
"outputs": [],
"source": [
"All = All.drop(columns=\"clear\")\n",
"# drop one dummy variable to avoid perfect collinearity"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import KFold\n",
"kf = KFold(n_splits=10,shuffle=True)\n",
"All = All.to_numpy()\n",
"y = y.to_numpy()"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RMSE on 10-fold CV on the test: 156.45060521124321\n",
"RMSE on 10-fold CV on the train: 156.4041159922115\n"
]
}
],
"source": [
"err_train = 0\n",
"err_test =0\n",
"for train,test in kf.split(All):\n",
" lr=linear_model.LinearRegression()\n",
" reg=lr.fit(All[train],y[train])\n",
" y_pred_train =reg.predict(All[train])\n",
" y_pred_test =reg.predict(All[test])\n",
" e_train= y[train]-y_pred_train\n",
" e_test = y[test]-y_pred_test\n",
" err_test += np.sqrt(np.mean(e_test*e_test)) \n",
" err_train += np.sqrt(np.mean(e_train*e_train)) \n",
"rmse_10cv_test = err_test/10 \n",
"rmse_10cv_train = err_train/10 \n",
"print('RMSE on 10-fold CV on the test: {}'.format(rmse_10cv_test))\n",
"print('RMSE on 10-fold CV on the train: {}'.format(rmse_10cv_train))"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MAE on 10-fold CV on the train data: 116.76353395090612\n",
"MAE on 10-fold CV on the test data: 116.6606355068355\n"
]
}
],
"source": [
"err_train = 0\n",
"err_test =0\n",
"for train,test in kf.split(All):\n",
" lr=linear_model.LinearRegression()\n",
" reg=lr.fit(All[train],y[train])\n",
" y_pred_train =reg.predict(All[train])\n",
" y_pred_test =reg.predict(All[test])\n",
" e_train= y[train]-y_pred_train\n",
" e_test = y[test]-y_pred_test\n",
" err_train += metrics.mean_absolute_error(y[train], reg.predict(All[train])) \n",
" err_test += metrics.mean_absolute_error(y[test], reg.predict(All[test]))\n",
"mae_10cv_test = err_train/10 \n",
"mae_10cv_train = err_test/10 \n",
"print('MAE on 10-fold CV on the train data: {}'.format(mae_10cv_train))\n",
"print('MAE on 10-fold CV on the test data: {}'.format(mae_10cv_test))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question 1.4 :"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Should we use all features or only the two features? Why? Is there the problem of overfitting in the model you select? Why? [10 Points]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ans: From the RMSE and MAE, the model with all features has less RMSE and MAE, so we should use the model with all feature\n",
"as it fits our dependent variable better. There might be the problem of overfitting in the model of all features because some of the independent variables are similar to each other i.e, temp and atemp."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question 1.5 :"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the model you selcted in 1.4 to create the dataframe that contains three columns which are : Actual value, Predicted value, and the error/residual. Then fill in these values from the test data.[10 Points]"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [],
"source": [
"#Here is the space for your codes\n",
"predict = lr.predict(All)"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "Per-column arrays must each be 1-dimensional",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"Input \u001b[1;32mIn [123]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m table \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDataFrame\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPredicted value\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mpredict\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mActual value\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43merror/residual\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:636\u001b[0m, in \u001b[0;36mDataFrame.__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m 630\u001b[0m mgr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_mgr(\n\u001b[0;32m 631\u001b[0m data, axes\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindex\u001b[39m\u001b[38;5;124m\"\u001b[39m: index, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m: columns}, dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy\n\u001b[0;32m 632\u001b[0m )\n\u001b[0;32m 634\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m 635\u001b[0m \u001b[38;5;66;03m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[39;00m\n\u001b[1;32m--> 636\u001b[0m mgr \u001b[38;5;241m=\u001b[39m \u001b[43mdict_to_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmanager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 637\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, ma\u001b[38;5;241m.\u001b[39mMaskedArray):\n\u001b[0;32m 638\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mma\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmrecords\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmrecords\u001b[39;00m\n",
"File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py:502\u001b[0m, in \u001b[0;36mdict_to_mgr\u001b[1;34m(data, index, columns, dtype, typ, copy)\u001b[0m\n\u001b[0;32m 494\u001b[0m arrays \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m 495\u001b[0m x\n\u001b[0;32m 496\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x\u001b[38;5;241m.\u001b[39mdtype, ExtensionDtype)\n\u001b[0;32m 497\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m x\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m 498\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m arrays\n\u001b[0;32m 499\u001b[0m ]\n\u001b[0;32m 500\u001b[0m \u001b[38;5;66;03m# TODO: can we get rid of the dt64tz special case above?\u001b[39;00m\n\u001b[1;32m--> 502\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marrays_to_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtyp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconsolidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py:120\u001b[0m, in \u001b[0;36marrays_to_mgr\u001b[1;34m(arrays, columns, index, dtype, verify_integrity, typ, consolidate)\u001b[0m\n\u001b[0;32m 117\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verify_integrity:\n\u001b[0;32m 118\u001b[0m \u001b[38;5;66;03m# figure out the index, if necessary\u001b[39;00m\n\u001b[0;32m 119\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 120\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[43m_extract_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 121\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 122\u001b[0m index \u001b[38;5;241m=\u001b[39m ensure_index(index)\n",
"File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py:661\u001b[0m, in \u001b[0;36m_extract_index\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m 659\u001b[0m raw_lengths\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mlen\u001b[39m(val))\n\u001b[0;32m 660\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(val, np\u001b[38;5;241m.\u001b[39mndarray) \u001b[38;5;129;01mand\u001b[39;00m val\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 661\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPer-column arrays must each be 1-dimensional\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 663\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m indexes \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m raw_lengths:\n\u001b[0;32m 664\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIf using all scalar values, you must pass an index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"\u001b[1;31mValueError\u001b[0m: Per-column arrays must each be 1-dimensional"
]
}
],
"source": [
"table = pd.DataFrame({\"Predicted value\": predict, \"Actual value\":y, \"error/residual\":y-predict})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question 2 [20 points]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question 2.1:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2.1 The below information shows the U.S unemployment rate (unemp)in 2010 from January-December \n",
"\n",
"\n",
"Task: write down the python code using control flow \"if\" to print the message ONLY the months that have the unemployment rate above 8% i.e.\n",
"\n",
"\n",
"- The umemployment rate in Apr is 14.8%\n",
"\n",
"- The unemployment rate in May is 13.3%\n",
"\n",
"\n",
"\n",
"[10 Points]"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"unemp= [3.5,\n",
"3.5,\n",
"4.4,\n",
"14.8,\n",
"13.3,\n",
"11.1,\n",
"10.2,\n",
"8.4,\n",
"7.8,\n",
"6.9,\n",
"6.7,\n",
"6.7]\n",
"month =['Jan','Feb','Mar','Apr','May','June','July','Aug','Sep','Oct','Nov','Dec']"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the unemployment rate in Apr is 14.8 %\n",
"the unemployment rate in May is 13.3 %\n",
"the unemployment rate in June is 11.1 %\n",
"the unemployment rate in July is 10.2 %\n",
"the unemployment rate in Aug is 8.4 %\n"
]
}
],
"source": [
"#Here is the space for your codes\n",
"for i in range(len(unemp)):\n",
" if unemp[i] > 8:\n",
" print('the unemployment rate in', month[i],'is', unemp[i],\"%\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question 2.2 :"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2.2 write the list [.] of even numbers from 2-500. Then using control flow to find out the summation of the log of even numbers from 2-100. i.e. log(2)+log(4)+....log(100) \n",
"[10 Points]"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The list of even number is [2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200, 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238, 240, 242, 244, 246, 248, 250, 252, 254, 256, 258, 260, 262, 264, 266, 268, 270, 272, 274, 276, 278, 280, 282, 284, 286, 288, 290, 292, 294, 296, 298, 300, 302, 304, 306, 308, 310, 312, 314, 316, 318, 320, 322, 324, 326, 328, 330, 332, 334, 336, 338, 340, 342, 344, 346, 348, 350, 352, 354, 356, 358, 360, 362, 364, 366, 368, 370, 372, 374, 376, 378, 380, 382, 384, 386, 388, 390, 392, 394, 396, 398, 400, 402, 404, 406, 408, 410, 412, 414, 416, 418, 420, 422, 424, 426, 428, 430, 432, 434, 436, 438, 440, 442, 444, 446, 448, 450, 452, 454, 456, 458, 460, 462, 464, 466, 468, 470, 472, 474, 476, 478, 480, 482, 484, 486, 488, 490, 492, 494, 496, 498, 500]\n",
"The sum of log of even number is 1307.3320269308394\n"
]
}
],
"source": [
"#Here is the space for your codes\n",
"Sum = 0\n",
"Even_number = []\n",
"for i in range(2,501):\n",
" if i % 2 == 1:\n",
" continue\n",
" Even_number.append(i)\n",
" Sum = Sum + np.log(i)\n",
"print('The list of even number is',Even_number)\n",
"print('The sum of log of even number is',Sum)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question 3 [20 point]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3.1 Consider the stock prices: TESLA (TSLA) stock.from June 29, 2010 to Sept 30, 2022. The data are downloadable from Yahoo Finance."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot (1) the TESLA prices and (2) TESLA log returns. Then, check the stationarity condition of these two series. [10 points]"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
"#Here is the space for your codes\n",
"import pandas_datareader.data as web\n",
"TESLA = web.DataReader('TSLA', 'yahoo', start='2010-06-29', end='2022-09-30')"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"TESLA[['Adj Close']].plot()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [],
"source": [
"TESLA['return'] = np.log(TESLA[['Adj Close']]).diff()"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"TESLA[['return']].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3.2 Using the library seaborn to plot the distribution of TESLA log returns. [10 points]"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#Here is the space for your codes\n",
"sns.displot(TESLA, x=\"return\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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",
"version": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}