Approach:

1. Sentiment Analysis using Logistic Regression:

For sentiment analysis of the stock market using logistic regression, we followed a structured approach:

• Data Collection: Textual data related to the stock market was collected

  
  
  from various articles published on financial news websites and journals. These articles provided valuable insights into market sentiment, capturing opinions, analyses, and discussions surrounding specific stocks or broader market trends.

• Data Preprocessing: The textual data extracted from articles underwent rigorous preprocessing to ensure its suitability for sentiment analysis. This involved cleaning the text by removing special characters, punctuation, and stopwords. Additionally, the text was tokenized and transformed into numerical features using techniques like TF-IDF (Term Frequency-Inverse Document Frequency).

• Model Training: The preprocessed data was split into training and testing sets. The logistic regression model was trained on the labeled training data derived from articles, where each text sample was associated with a sentiment label (positive or negative). Techniques like cross-validation were employed to tune hyperparameters and optimize model performance.

• Model Evaluation: The trained logistic regression model was evaluated on the testing data to assess its performance in predicting sentiment polarity. Metrics such as accuracy, precision, recall, and F1 score were utilized to gauge the model's effectiveness in capturing sentiment from textual data extracted from articles.

2. Stock Market Prediction using LSTM Networks:

For predicting stock market movements using LSTM networks, we adopted the following approach:

• Data Collection: Historical stock market data and related textual information were sourced from Kaggle, a reputable platform known for hosting diverse datasets relevant to machine learning and data analysis. This dataset encompassed a wide range of textual sources, including news headlines, financial reports, and sentiment scores, complementing the quantitative stock market data.

• Data Preprocessing: The stock market dataset obtained from Kaggle underwent preprocessing steps tailored to LSTM input. This included structuring textual data into sequences suitable for LSTM input, tokenization, removing stopwords, and converting words into word embeddings.

• Model Architecture: We designed a customized LSTM network architecture suited for stock market prediction. This architecture comprised layers for word embeddings, LSTM cells, and output layers for forecasting future stock prices or market trends.

• Model Training: The dataset was split into training, validation, and testing sets. The LSTM model was trained on the training data sourced from Kaggle, with hyperparameters fine-tuned based on performance on the validation set. Techniques like dropout regularization were employed to prevent overfitting.

• Model Evaluation: The trained LSTM model's performance was evaluated on the testing data to assess its predictive accuracy. Metrics such as mean squared error (MSE) or mean absolute error (MAE) were used to measure the model's ability to forecast stock prices or market trends accurately.