This repository contains three different machine learning models:
Movie Genre Classification - Predicts the genre of a movie based on its plot summary.
Customer Churn Prediction - Predicts whether a customer will churn based on their usage and demographic data.
Handwritten-Like Text Generation - Generates text based on a given seed using a Markov-based approach.
- Movie Genre Classification
Description
This model predicts the genre of a movie based on a given plot summary. It uses the TF-IDF vectorization technique and a Logistic Regression classifier.
Requirements
Python 3.x
numpy
pandas
sklearn
Usage
Run the script and enter a movie plot when prompted:
python movie_genre_classifier.py
Enter a plot description, and the model will output the predicted genre.
- Customer Churn Prediction
This model predicts whether a customer will churn based on features like monthly usage, customer age, and subscription length. It uses a Random Forest Classifier.
Requirements
Python 3.x
numpy
pandas
sklearn
Usage
Run the script and input customer details when prompted:
python customer_churn_prediction.py
Enter the required customer details, and the model will predict if the customer is likely to churn.
- Handwritten-Like Text Generation
Description
This model generates text using a Markov-based approach that selects characters randomly based on prior sequences.
Requirements
Python 3.x
numpy
Usage
Run the script and enter a seed text when prompted:
python text_generation.py
The model will generate new text based on the provided input.
Notes
Each script runs interactively in the terminal.
Datasets should be replaced with real-world data for better accuracy.
The text generation model can be improved by incorporating deep learning techniques like LSTMs.