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

# ## <u>Name </u> : ADVAIT GURUNATH CHAVAN
# ## <u>Contact Number </u> : +91 70214 55852
# ## <u>Mail ID </u> : advaitchavan135@gmail.com 
# 
# ## Oasis Infobyte Data Science Intern
# 
# ## Task 5 : Sales Predicition using Python

# <img src = "demo.jpg">

# ## 1. Importing the necessary dependencies

# In[3]:


import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import plotly.io as plio
plio.templates
import plotly.express as px
import plotly.graph_objects as go
from sklearn.model_selection import train_test_split

from sklearn.metrics import mean_squared_error, r2_score

from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor

from xgboost import XGBRegressor

import joblib

from warnings import filterwarnings
filterwarnings(action='ignore')


# ## 2. Exploring the dataset

# In[4]:


data = pd.read_csv('advertising.csv')


# In[5]:


data


# In[6]:


data.info()


# ### Removing the unnecessary column Unnamed: 0

# In[7]:


data.drop('Unnamed: 0', axis=1, inplace=True)


# In[8]:


data
#data.to_csv('advertising_cleaned.csv', index=False)


# In[9]:


data.info()


# In[10]:


data.describe()


# In[11]:


data.duplicated().sum()


# ### Correlation plot to visualize the impact of each feature on target(sales)

# In[12]:


sns.heatmap(data.corr(),annot=True)


# #### From the above correlation plot we have feature: -
# #### 1. 'TV' having an impact score of 0.78 on Sales
# #### 2. 'Radio' having an impact score of 0.58 on Sales
# #### 3. 'Newspaper' having an impact score of 0.23 on Sales

# ### [A]Plotly Scatterplot of TV(feature) vs Sales(Target)

# In[13]:


# Create a scatter plot
fig = go.Figure(data=go.Scatter(x=data['TV'], y=data['Sales'], mode='markers', marker=dict(color='orange', size=8)))

# Customize the layout
fig.update_layout(
    title="Scatter Plot of TV(Feature) vs Sales(Target)",
    xaxis_title="TV",
    yaxis_title="Sales"
)

# Show the plot
fig.show()


# #### From the above scatterplot we can infer that 
# #### As the score of TV is increasing score of Sales is also increasing

# ### [B]Plotly Scatterplot of Radio(feature) vs Sales(Target)

# In[16]:


# Create a scatter plot
fig = go.Figure(data=go.Scatter(x=data['Radio'], y=data['Sales'], mode='markers', marker=dict(color='red', size=8)))

# Customize the layout
fig.update_layout(
    title="Scatter Plot of Radio(Feature) vs Sales(Target)",
    xaxis_title="Radio",
    yaxis_title="Sales"
)

# Show the plot
fig.show()


# #### From the above scatterplot we can infer that 
# #### As the score of Radio is increasing score of Sales is also increasing

# ### [C]Plotly Scatterplot of Newspaper(feature) vs Sales(Target)

# In[17]:


# Create a scatter plot
fig = go.Figure(data=go.Scatter(x=data['Newspaper'], y=data['Sales'], mode='markers', marker=dict(color='blue', size=8)))

# Customize the layout
fig.update_layout(
    title="Scatter Plot of Newspaper(Feature) vs Sales(Target)",
    xaxis_title="Newspaper",
    yaxis_title="Sales"
)

# Show the plot
fig.show()


# ### [i] Distribution of TV feature

# In[126]:


plt.figure(figsize=(10, 6))
ax = sns.histplot(data['TV'], bins=20, kde=True, color='orange')
plt.xlabel('TV')
plt.ylabel('Frequency')
plt.title('Distribution of TV feature')
for p in ax.patches:
    ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),
                ha='center', va='center', fontsize=10, color='black', xytext=(0, 10),
                textcoords='offset points')
plt.show()


# ### [ii] Distribution of Radio feature

# In[46]:


plt.figure(figsize=(10, 6))
ax = sns.histplot(data['Radio'], bins=20, kde=True, color='red')
plt.xlabel('Radio')
plt.ylabel('Frequency')
plt.title('Distribution of Radio feature')
for p in ax.patches:
    ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),
                ha='center', va='center', fontsize=10, color='black', xytext=(0, 10),
                textcoords='offset points')
plt.show()


# ### [iii] Distribution of Newspaper feature

# In[47]:


plt.figure(figsize=(10, 6))
ax = sns.histplot(data['Newspaper'], bins=20, kde=True)
plt.xlabel('Newspaper')
plt.ylabel('Frequency')
plt.title('Distribution of Newspaper feature')
for p in ax.patches:
    ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),
                ha='center', va='center', fontsize=10, color='black', xytext=(0, 10),
                textcoords='offset points')
plt.show()


# ### [iv] Distribution of Sales

# In[48]:


plt.figure(figsize=(10, 6))
ax = sns.histplot(data['Sales'], bins=20, kde=True, color='darkviolet')
plt.xlabel('Sales')
plt.ylabel('Frequency')
plt.title('Distribution of Sales(target)')
for p in ax.patches:
    ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),
                ha='center', va='center', fontsize=10, color='black', xytext=(0, 10),
                textcoords='offset points')
plt.show()


# ### 3. Preparing data for Training (Data Modelling)
# 
# #### Since, we need to predict the Sales using different features of the dataset
# #### We consider Sales as a seperate dataset say 'y' and all other features as 'x'

# In[54]:


x = data.iloc[:,:3]
y = data.iloc[:,3:]


# In[53]:


x


# In[55]:


y


# In[58]:


x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)


# ### 4.Training Models using the above data

# #### [A] Linear Regression Model

# In[59]:


model_1 = LinearRegression()
model_1.fit(x_train, y_train)
y_pred_1 = model_1.predict(x_test)


# In[60]:


mse = mean_squared_error(y_test, y_pred_1)
r2 = r2_score(y_test, y_pred_1)
print("Mean Square Error is :", mse)
print("R-Squared score is :",r2)


# In[61]:


plt.figure(figsize=(10, 6))
plt.scatter(y_test, y_pred_1, alpha=0.5, color='darkviolet')
plt.xlabel('Actual Sales')
plt.ylabel('Predicted Sales')
plt.title('Actual vs. Predicted Sales (Linear Regression)')
plt.show()


# #### [B] Random Forest Regression Model

# In[62]:


model_2 = RandomForestRegressor() 
model_2.fit(x_train, y_train) 
y_pred_2 = model_2.predict(x_test)


# In[63]:


mse = mean_squared_error(y_test, y_pred_2)
r2 = r2_score(y_test, y_pred_2)
print("Mean Square Error is :", mse)
print("R-Squared score is :",r2)


# In[65]:


plt.figure(figsize=(10, 6))

plt.scatter(y_test, y_pred_2, label=model_2, alpha=0.5, color='green')
plt.xlabel('Actual Sales')
plt.ylabel('Predicted Sales')
plt.title('Actual vs. Predicted Sales (Random Forest Regression)')

plt.legend()
plt.show()


# #### [C] Ridge Regression Model

# In[66]:


model_3 = Ridge() 
model_3.fit(x_train, y_train) 
y_pred_3 = model_3.predict(x_test)


# In[67]:


mse = mean_squared_error(y_test, y_pred_3)
r2 = r2_score(y_test, y_pred_3)
print("Mean Square Error is :", mse)
print("R-Squared score is :",r2)


# In[68]:


plt.figure(figsize=(10, 6))

plt.scatter(y_test, y_pred_3, label=model_3, alpha=0.5, color='darkblue')

plt.xlabel('Actual Sales')
plt.ylabel('Predicted Sales')
plt.title('Actual vs. Predicted Sales (Ridge Regression)')
plt.legend()
plt.show()


# #### [D] Lasso Regression Model

# In[69]:


model_4 = Lasso() 
model_4.fit(x_train, y_train) 
y_pred_4 = model_4.predict(x_test)


# In[70]:


mse = mean_squared_error(y_test, y_pred_4)
r2 = r2_score(y_test, y_pred_4)
print("Mean Square Error is :", mse)
print("R-Squared score is :",r2)


# In[71]:


plt.figure(figsize=(10, 6))

plt.scatter(y_test, y_pred_4, label=model_4, alpha=0.5, color='black')
plt.xlabel('Actual Sales')
plt.ylabel('Predicted Sales')
plt.title('Actual vs. Predicted Sales (Lasso Regression)')

plt.legend()
plt.show()


# #### [E] ElasticNet Regression Model

# In[72]:


model_5 = ElasticNet() 
model_5.fit(x_train, y_train) 
y_pred_5 = model_5.predict(x_test)


# In[73]:


mse = mean_squared_error(y_test, y_pred_5)
r2 = r2_score(y_test, y_pred_5)
print("Mean Square Error is :", mse)
print("R-Squared score is :",r2)


# In[104]:


plt.figure(figsize=(10, 6))

plt.scatter(y_test, y_pred_5, label=model_5, alpha=0.5, color='gold')
plt.xlabel('Actual Sales')
plt.ylabel('Predicted Sales')
plt.title('Actual vs. Predicted Sales (ElasticNet Regression)')
plt.legend()
plt.show()


# #### [F] Gradient Boosting Regression Model

# In[75]:


model_6 = GradientBoostingRegressor() 
model_6.fit(x_train, y_train) 
y_pred_6 = model_6.predict(x_test)


# In[76]:


mse = mean_squared_error(y_test, y_pred_6)
r2 = r2_score(y_test, y_pred_6)
print("Mean Square Error is :", mse)
print("R-Squared score is :",r2)


# In[77]:


plt.figure(figsize=(10, 6))

plt.scatter(y_test, y_pred_6, label=model_6, alpha=0.5, color='red')

plt.xlabel('Actual Sales')
plt.ylabel('Predicted Sales')
plt.title('Actual vs. Predicted Sales (Gradient Boosting Regression)')
plt.legend()
plt.show()


# #### [G] XGBoost Regression Model

# In[78]:


model_7 = XGBRegressor()
model_7.fit(x_train, y_train) 
y_pred_7 = model_7.predict(x_test)


# In[79]:


mse = mean_squared_error(y_test, y_pred_7)
r2 = r2_score(y_test, y_pred_7)
print("Mean Square Error is :", mse)
print("R-Squared score is :",r2)


# In[80]:


plt.figure(figsize=(10, 6))

plt.scatter(y_test, y_pred_7, label='XGBoost Regression Model', alpha=0.5, color='brown')

plt.xlabel('Actual Sales')
plt.ylabel('Predicted Sales')
plt.title('Actual vs. Predicted Sales (XGBoost Regression)')
plt.legend()
plt.show()


# ##### Model r^2 scores of each regression model

# In[81]:


model_r2_scores = {
     "Linear Regression Model":  r2_score(y_test, y_pred_1),

     "Random Forest Regression Model":  r2_score(y_test, y_pred_2),

     "Ridge Regression Model":  r2_score(y_test, y_pred_3),

     "Lasso Regression Model":  r2_score(y_test, y_pred_4),

     "ElasticNet Regression Model":  r2_score(y_test, y_pred_5),

     "Gradient Boosting Regression Model": r2_score(y_test, y_pred_6),

     "XGBoost Regression Model":  r2_score(y_test, y_pred_7)
}


# In[93]:


model_r2_scores


# In[90]:


best_model_name = max(model_r2_scores, key=model_r2_scores.get)
best_r2_score = model_r2_scores[best_model_name]

print(f"Best Performing Model is {best_model_name} with an R^2 score of {best_r2_score}")


# ### 5. Saving the Model having the best fit

# In[83]:


final_model = model_6
joblib.dump(final_model, 'gradient_boosting_model.pkl')


# In[89]:


feature_importances = pd.Series(final_model.feature_importances_, index=x.columns)
plt.figure(figsize=(10, 6))
features = feature_importances
features.plot(kind='bar')
plt.xlabel('Feature')
plt.ylabel('Feature Importance Score')
plt.title('Features having impact on the Sales(recognised by Gradient Boosting Regression Model)')
for index, value in enumerate(features):
    plt.text(index, value, f'{value:.2f}', ha='center', va='bottom')
plt.show()


# In[ ]: