Description:

This Kaggle project focuses on predicting the ratings of recipes based on various features such as UserReputaiton, User's Likes, dislikes, replies , user reviews and many more. This project aims to develop a machine learning model that reliably estimates the rating of a recipe given its attributes.

Key Features:

Data preprocessing: Cleaning and organizing the dataset, handling missing values, and processing categorical and numerical variables.

Exploratory Data Analysis (EDA): Analyzing the distribution of recipe ratings, exploring relationships between features, and identifying patterns in the data.

Feature engineering: Creating new features or transforming existing ones to improve model performance.

Model selection and training: Experimenting with various machine learning algorithms such as Logistic regression, Random Forest trees, and KNearestNeighbors to find the best-performing model.

Model evaluation: Assessing model performance using Bar charts and Classification reports.

Technologies Used:

Python: Pandas, NumPy,Seaborn, Matplott, regex scikit-learn for data preprocessing, modeling, and evaluation.

Colab/Kaggle/Jupyter Notebook: for project development and documentation.