#importing libraries import pandas as pd import numpy as np from scipy import stats from scipy.stats import kurtosistest import plotly.express as px import matplotlib.pyplot as plt import seaborn as sns from pandas.plotting import scatter_matrix import warnings %matplotlib inline warnings.simplefilter("ignore")

#reading the data train = pd.read_csv("Train.csv") test = pd.read_csv("Test.csv")

#checking on the basic print(train.head(2)) print("*"*50) print(train.isnull().sum()) print("*"*50) print("The shape of Train Dataset is:",train.shape)

train.describe()

#checking normality of data k2, p = stats.normaltest(train['GrocerySales']) alpha = 1e-3 print("p = {:g}".format(p)) p = 3.27207e-11 if p < alpha: # null hypothesis: x comes from a normal distribution print("Data Not Normalize") else: print("Data Noramlize")

#correlation comparison corr = train.corr() corr.style.background_gradient(cmap='coolwarm')

#scatter matrix scatter_matrix(train) plt.show()

#checking on outliers plt.figure(figsize=(5,5)) sns.boxplot(x= 'variable', y = 'value', data = pd.melt(train[['GrocerySales']]))

#treemap fig = px.treemap(train, path=['GrocerySales'],color='GrocerySales', hover_data=['Day', 'GrocerySales']) fig.show()

#scatter plot fig = px.scatter(train, x="Day", y="GrocerySales", trendline="ols") fig.show()

#Grocery Sales density plot sns.set_style("darkgrid") sns.kdeplot(data=train['GrocerySales'],label="GrocerySales" ,shade=True)

from sklearn.model_selection import train_test_split from sklearn.preprocessing import RobustScaler from sklearn.linear_model import LinearRegression from sklearn.neighbors import KNeighborsRegressor from sklearn.metrics import mean_squared_error, mean_absolute_error

X = train['Day'] y = train['GrocerySales'] X_pred = test['Day']

X = np.array(X) X = X.reshape(-1, 1)

X_pred = np.array(X_pred) X_pred = X_pred.reshape(-1, 1)

#scaling the data rs = RobustScaler()

X_scaled = rs.fit_transform(X) X_pred_scaled = rs.fit_transform(X_pred)

train_X, test_X, train_y, test_y = train_test_split(X, y,test_size=0.3,random_state = 42)

lr = LinearRegression() lr.fit(train_X,train_y)

predict = lr.predict(test_X) print('Mean Squared Error: %.2f'% mean_squared_error(test_y,predict)) print('Mean Absolute Error: %.2f'% mean_absolute_error(test_y,predict))

y_pred_lr = lr.predict(X_pred)

y_pred_lr

knn = KNeighborsRegressor() knn.fit(train_X,train_y)

predict_knn = knn.predict(test_X) print('Mean Squared Error: %.2f'% mean_squared_error(test_y,predict_knn)) print('Mean Absolute Error: %.2f'% mean_absolute_error(test_y,predict_knn))

y_pred_knn = knn.predict(X_pred)

y_pred_knn

scaled_train_X, scaled_test_X, train_y, test_y = train_test_split(X_scaled, y, test_size=0.3,random_state = 42)

lr = LinearRegression() lr.fit(scaled_train_X,train_y)

scaled_predict = lr.predict(scaled_test_X) print('Mean Squared Error: %.2f'% mean_squared_error(test_y,scaled_predict)) print('Mean Absolute Error: %.2f'% mean_absolute_error(test_y,scaled_predict))

y_pred_scaled_lr = lr.predict(X_pred_scaled)

y_pred_scaled_lr

knn = KNeighborsRegressor() knn.fit(scaled_train_X,train_y)

scaled_predict_knn = knn.predict(scaled_test_X) print('Mean Squared Error: %.2f'% mean_squared_error(test_y,scaled_predict_knn)) print('Mean Absolute Error: %.2f'% mean_absolute_error(test_y,scaled_predict_knn))

y_pred_scaled_knn = knn.predict(X_pred_scaled)

y_pred_scaled_knn

sns.distplot(test_y-predict) plt.title("Histogram of Residuals") plt.xlabel("Residuals") plt.ylabel("Frequency") plt.show()

sns.distplot(test_y-predict_knn) plt.title("Histogram of Residuals") plt.xlabel("Residuals") plt.ylabel("Frequency") plt.show()

sns.distplot(test_y-scaled_predict) plt.title("Histogram of Residuals") plt.xlabel("Residuals") plt.ylabel("Frequency") plt.show()

sns.distplot(test_y-scaled_predict_knn) plt.title("Histogram of Residuals") plt.xlabel("Residuals") plt.ylabel("Frequency") plt.show()

df1 = pd.DataFrame(y_pred_lr,columns=['GrocerySales_lr']) df2 = pd.DataFrame(y_pred_knn,columns=['GrocerySales_knn']) df3 = pd.DataFrame(y_pred_scaled_lr,columns=['GrocerySales_lrs']) df4 = pd.DataFrame(y_pred_scaled_knn,columns=['GrocerySales_knns'])

temp = [test, df1, df2, df3, df4]

test_result = pd.concat(temp, axis=1, join='outer', ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, copy=True)

test_result.head(2)

fig = px.scatter(x=test_result.Day, y=test_result.GrocerySales_knn) fig.show()

fig = px.scatter(x=test_result.Day, y=test_result.GrocerySales_knns) fig.show()

fig = px.scatter(x=test_result.Day, y=test_result.GrocerySales_lr) fig.show()

fig = px.scatter(x=test_result.Day, y=test_result.GrocerySales_lrs) fig.show()