Merge pull request #1031 from aditya-bhaumik/master

TCS Stock Price Prediction using lstm
Niketkumardheeryan · Aug 2, 2024 · efb6249 · efb6249
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diff --git a/TCS Stock Analysis/Dataset/TCS.csv b/TCS Stock Analysis/Dataset/TCS.csv
diff --git a/TCS Stock Analysis/Images/Distribution of TCS Daily Returns.png b/TCS Stock Analysis/Images/Distribution of TCS Daily Returns.png
diff --git a/TCS Stock Analysis/Images/K-Means Clustering of TCS Stock Price.png b/TCS Stock Analysis/Images/K-Means Clustering of TCS Stock Price.png
diff --git a/TCS Stock Analysis/Images/TCS High Stock Prices.png b/TCS Stock Analysis/Images/TCS High Stock Prices.png
diff --git a/TCS Stock Analysis/Images/TCS Stock Price Prediction using LSTM.png b/TCS Stock Analysis/Images/TCS Stock Price Prediction using LSTM.png
diff --git a/TCS Stock Analysis/Images/TCS Stock Price.png b/TCS Stock Analysis/Images/TCS Stock Price.png
diff --git a/...l/TCS_Stock_Price_Prediction_using_LSTM,_K_Means_Clustering_And_Logistic_Regression.ipynb b/...l/TCS_Stock_Price_Prediction_using_LSTM,_K_Means_Clustering_And_Logistic_Regression.ipynb
diff --git a/TCS Stock Analysis/README.md b/TCS Stock Analysis/README.md
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+# TCS Stock Price Prediction using LSTM, K-Means Clustering, and Logistic Regression
+
+## Overview
+
+This project aims to predict the stock prices of Tata Consultancy Services (TCS) using a combination of Long Short-Term Memory (LSTM) networks, K-Means Clustering, and Logistic Regression. The system integrates these techniques to analyze historical stock price data and forecast future trends, enabling better investment decisions.
+
+## Objectives
+
+- Data Processing: Clean and preprocess historical stock price data of TCS.
+- Model Development: Develop and train LSTM, K-Means Clustering, and Logistic Regression models for stock price prediction.
+- Evaluation: Assess model performance and prediction accuracy.
+- Optimization: Recommend optimal trading strategies based on model predictions.
+
+## Technologies Used
+
+- Python: Programming language for development.
+- TensorFlow/Keras: Deep learning frameworks for building and training the LSTM model.
+- Pandas: Data manipulation and analysis.
+- NumPy: Numerical computing.
+- Scikit-Learn: Machine learning tools for K-Means Clustering and Logistic Regression.
+- Matplotlib/Seaborn: Data visualization.
+
+## Models
+
+### Long Short-Term Memory (LSTM)
+- *Purpose*: Predict future stock prices by capturing temporal dependencies in historical data.
+- *Architecture*: LSTM layers to handle sequential data, followed by dense layers for output prediction.
+
+### K-Means Clustering
+- *Purpose*: Segment historical stock data into clusters to identify patterns and trends.
+- *Architecture*: K-Means algorithm to cluster data points based on similarities, aiding in feature extraction for predictive modeling.
+
+### Logistic Regression
+- *Purpose*: Classify stock price movements (e.g., up or down) based on extracted features.
+- *Architecture*: Logistic regression model to predict binary outcomes, using features derived from historical data and clustering results.
diff --git a/TCS Stock Analysis/requirements.txt b/TCS Stock Analysis/requirements.txt
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+**Requirements For Project :-**
+
+1. NumPy: Fundamental package for numerical computing.
+2. pandas: Data analysis and manipulation library.
+3. scikit-learn: Machine learning library for classification, regression, and clustering.
+4. Matplotlib: Plotting library for creating visualizations.
+5. Keras: High-level neural networks API, typically used with TensorFlow backend.
+6. seaborn: Statistical data visualization library based on Matplotlib.