This project is part of the Bharat Intern internship program and involves building a machine learning model that can accurately recognize handwritten digits (0–9) using the MNIST dataset. The MNIST dataset is a benchmark dataset in machine learning, containing 70,000 grayscale images of handwritten digits (60,000 for training and 10,000 for testing), each of size 28x28 pixels. Objectives: To preprocess and visualize the MNIST data. To build a deep learning model (typically using TensorFlow/Keras or PyTorch) for digit classification. To train the model on the training dataset. To evaluate model accuracy on the test set. To demonstrate predictions with sample handwritten digits. Key Components: Data Loading and Preprocessing: Importing the dataset. Normalizing the pixel values (scaling them between 0 and 1). Reshaping the data if necessary. Model Building: Creating a Convolutional Neural Network (CNN) or a simple feedforward Neural Network. Using activation functions like ReLU and softmax. Compiling the model with appropriate loss function and optimizer. Training the Model: Fitting the model to the training data. Monitoring accuracy and loss during training. Evaluation and Testing: Evaluating performance on the test set. Displaying the confusion matrix or accuracy metrics. Prediction and Visualization: Making predictions on unseen data. Visualizing the digits and model’s predictions. Outcome: A trained model capable of recognizing handwritten digits with high accuracy, demonstrating the practical application of deep learning for image classification tasks.

This project, part of the Bharat Intern internship program, focuses on using LSTM (Long Short-Term Memory) — a type of Recurrent Neural Network (RNN) — to predict future stock prices based on historical data. The goal is to build a deep learning model that learns patterns and trends from past stock prices and provides forecasts for upcoming values. Objectives: To collect or load historical stock price data (typically using Yahoo Finance or CSV files). To preprocess and scale the data for neural network input. To build and train an LSTM model for time series forecasting. To evaluate model performance using metrics like RMSE (Root Mean Squared Error). To visualize predicted vs actual stock prices. Key Components: Data Collection & Preprocessing: Load stock price data (e.g., closing prices). Handle missing values and normalize using MinMaxScaler. Create sequences (X) and corresponding targets (Y) for LSTM training. LSTM Model Building: Construct an LSTM network using Keras/TensorFlow. Define layers (e.g., LSTM, Dense) and activation functions. Compile the model with loss function (like MSE) and optimizer (e.g., Adam). Model Training: Train the model on historical data with validation. Use train/test split to evaluate generalization. Prediction & Visualization: Predict future stock values using the trained model. Plot graphs comparing actual vs predicted prices. Outcome: A functional LSTM model that can forecast stock price trends with reasonable accuracy, demonstrating the power of deep learning in financial time series prediction.