This repository contains code for analyzing product ratings and generating recommendations using machine learning techniques. Prerequisites: Python 3.6 or higher,Pandas,Scikit-learn,Seaborn,Matplotlib Installation: pip install pandas scikit-learn seaborn matplotlib Usage: 1)Download the dataset "Df.csv" and place it in the same directory as the code. 2)Run the code using the following command: python main.py The code will perform the following tasks:
1)Load the dataset 2)Perform exploratory data analysis 3)Train machine learning models for predicting ratings 4)Evaluate the models on a test set 5)Calculate correlation matrix 6)Analyze product ratings distribution 7)Visualize KMeans clustering results 8)Generate sample recommendations
The code will generate the following output:
1)Descriptive statistics of the dataset 2)Summary of the dataset 3)Number of unique values in each categorical column 4)Silhouette score for KMeans clustering 5)Model scores for linear regression, artificial neural network, and Random Forest 6)Heatmap for correlation matrix 7)Histogram of product ratings 8)Scatter plot of product ratings vs. amount 9)Scatter plot of product ratings vs. quantity with color-coded clusters 10)Sample recommendations