This repository contains a classification model trained to classify Bollywood stars into different categories based on their attributes. The model is designed to predict the category of a given Bollywood star based on features such as age, gender, number of movies, social media presence, awards won, etc.
-> Introduction
-> Dataset
-> Model Architecture
-> Dependencies
Bollywood, the Indian film industry, is home to numerous talented actors and actresses. Classifying Bollywood stars into categories can be useful for various applications, such as recommendation systems, talent management, and market analysis. This classification model leverages machine learning techniques to predict the category of a Bollywood star based on their characteristics.
The dataset used to train the classification model is not included in this repository due to copyright and licensing restrictions. However, you can use a similar dataset with relevant features for training the model.
The dataset should be in a structured format, preferably a CSV file.
Ensure that the dataset is divided into features (input) and labels (output). In this case, the features represent the attributes of Bollywood stars, and the labels represent the categories they belong to.
The classification model used in this project is a supervised machine learning model. The specific architecture used will depend on the dataset and problem statement. For example, some common classification algorithms that can be used are:
-> Logistic Regression
-> Random Forest
-> Support Vector Machine (SVM)
-> Neural Networks (e.g., Multi-Layer Perceptron)
The model architecture should be chosen based on the complexity of the problem, the size of the dataset, and the computational resources available.
The following dependencies are required to run the classification model:
-> Python (>= 3.x)
-> NumPy
-> Pandas
-> Scikit-learn
You can install the required packages using the following command:
pip install numpy pandas scikit-learn