A collection of Machine Learning and Natural Language Processing (NLP) projects developed using Python, TensorFlow, and Jupyter Notebooks. This repository contains implementations of neural networks, recurrent neural networks, and sentiment analysis projects.
Machine-Learning/
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├── README.md
├── Digit Recognition Training(NN).ipynb
├── User spending prediction(RNN).ipynb
├── Khalti Sentiment Analysis.ipynb
└── NITSentimentAnalysis.ipynb
A handwritten digit recognition model trained on the MNIST dataset using TensorFlow and a Multilayer Perceptron (MLP).
Features
- MNIST dataset preprocessing
- Data normalization
- Feed-forward neural network
- Model training and evaluation
- Real-time digit prediction
Technologies
- TensorFlow
- NumPy
- Matplotlib
A Recurrent Neural Network (RNN) model for sequential user spending prediction.
Topics Covered
- Sequential data processing
- Hidden states
- Time-series learning
- Model training and evaluation
Sentiment analysis of Google Play Store reviews for the Khalti application using Natural Language Processing.
Features
- Data preprocessing
- Text cleaning
- Sentiment classification
- Word cloud generation
- Rating distribution analysis
- Positive vs. Negative review comparison
- Data visualization
Libraries
- Pandas
- NumPy
- Matplotlib
- NLTK
- WordCloud
Sentiment analysis and visualization of reviews related to NIT Delhi using Natural Language Processing techniques.
Features
- Review preprocessing
- Sentiment analysis
- Data visualization
- Statistical insights
- Python
- TensorFlow
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- NLTK
- WordCloud
git clone https://github.com/AASHISH-KHANIYA/Machine-Learning.gitpip install tensorflow pandas numpy matplotlib nltk wordcloudOpen any .ipynb file using Jupyter Notebook or Visual Studio Code.
Aashish Khaniya
This repository is intended for learning, experimentation, and educational purposes.