Sales Prediction using Python.py

#!/usr/bin/env python
# coding: utf-8

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import warnings
warnings.filterwarnings('ignore')

import numpy as np
import pandas as pd

# Data Visualisation
import matplotlib.pyplot as plt 
import seaborn as sns


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advertising = pd.DataFrame(pd.read_csv("C:/Users/prade/Downloads/advertising.csv"))
advertising.head()


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advertising.shape


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advertising.info()


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advertising.info()


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advertising.isnull().sum()*100/advertising.shape[0]


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fig, axs = plt.subplots(3, figsize = (5,5))
plt1 = sns.boxplot(advertising['TV'], ax = axs[0])
plt2 = sns.boxplot(advertising['Newspaper'], ax = axs[1])
plt3 = sns.boxplot(advertising['Radio'], ax = axs[2])
plt.tight_layout()


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sns.boxplot(advertising['Sales'])
plt.show()


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sns.pairplot(advertising, x_vars=['TV', 'Newspaper', 'Radio'], y_vars='Sales', height=4, aspect=1, kind='scatter')
plt.show()


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sns.heatmap(advertising.corr(), cmap="YlGnBu", annot = True)
plt.show()


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X = advertising['TV']
y = advertising['Sales']


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from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.7, test_size = 0.3, random_state = 100)


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X_train.head()


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y_train.head()


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import statsmodels.api as sm


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X_train_sm = sm.add_constant(X_train)
lr = sm.OLS(y_train, X_train_sm).fit()


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lr.params


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print(lr.summary())


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plt.scatter(X_train, y_train)
plt.plot(X_train, 6.948 + 0.054*X_train, 'r')
plt.show()


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y_train_pred = lr.predict(X_train_sm)
res = (y_train - y_train_pred)


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fig = plt.figure()
sns.distplot(res, bins = 15)
fig.suptitle('Error Terms', fontsize = 15)                  # Plot heading 
plt.xlabel('y_train - y_train_pred', fontsize = 15)         # X-label
plt.show()


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plt.scatter(X_train,res)
plt.show()


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# Add a constant to X_test
X_test_sm = sm.add_constant(X_test)

# Predict the y values corresponding to X_test_sm
y_pred = lr.predict(X_test_sm)


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y_pred.head()


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from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score


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np.sqrt(mean_squared_error(y_test, y_pred))


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r_squared = r2_score(y_test, y_pred)
r_squared


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plt.scatter(X_test, y_test)
plt.plot(X_test, 6.948 + 0.054 * X_test, 'r')
plt.show()


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