Facebook Ad-Campaigns Analysis /Sales-Prediction - Project B - python
In this project, I did Sales Conversion Optimization by analyzing Facebook ad campaigns Prediction using Random Forest Regressor Aim: To optimize Sales conversion and predict future sales. Approach: Exploratory Data Analysis using matplotlib and seaborn and model training using Random Forest Regressor. Importing Libraries import numpy as np import pandas as pd import os df=pd.read_csv("File/path.csv") df.head() df.info() #Checking for null values # Doing Exploratory Data Analysis df.shape (1143, 11) df . describe () import matplotlib.pyplot as plt import seaborn as sns Correlation Matrix : import matplotlib.pyplot as plt import seaborn as sns g=sns.heatmap(df[["Impressions","Clicks","Spent","Total_Conversion", "Approved_Conversion"]].corr(),annot=True ,fmt=".2f", cmap="coolwarm") # Here above it's clear, "Impressions" and "Total_Conversion" are more correlated...