stock market prediction using LSTM ankit

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1. Industrial Training II Ankit Shukla 202100289 SECTION - B Complete machine Learning , NLP Bootcamp MLOPS & Deployment UDEMY

2. CONTENTS Content Page No.

3. This training program offers an in-depth introduction to data science and machine learning, emphasizing key concepts like descriptive statistics, machine learning principles, regression, classification, and clustering techniques. Participants gain practical experience in supervised and unsupervised learning, data visualization with Tableau, and the application of these skills in real-world scenarios, equipping them for success in data-driven industries. ABSTRACT

4. Introduction Comprehensive foundation in data science and machine learning. Covers descriptive statistics and machine learning methodologies. Focuses on practical techniques for regression, classification, and clustering. Hands-on exercises for real-world application. Includes data visualization training using Tableau. Equips participants with skills for effective data analysis and decision-making.

5. Existing Datasets : Search for publicly available datasets related to your problem. Data Scraping : Gather data from websites, APIs, or other online sources. Manual Collection : Collect data manually through surveys, forms, or human input. Import Libraries : Use libraries like Pandas for data handling. Load Data : Read data into a DataFrame using pd.read_csv () . Data Cleaning : Handle missing values with df.dropna () or imputation. Remove duplicates with df.drop_duplicates () . Detect and handle outliers using statistical methods. Data Transformation : Encode categorical variables (one-hot or label encoding). Standardize or normalize numerical features with sklearn.preprocessing . Feature Engineering : Create new features from existing ones. Perform text cleaning (lowercase, removal of special characters) if needed. Data Conversion : Ensure data types are appropriate. Save Cleaned Data to CSV : Use df.to_csv ('cleaned_data.csv', index=False) .

6. STOCK PREDICTION USING LSTM Aim : To develop a predictive model that forecasts stock prices using Long Short-Term Memory (LSTM) neural networks, leveraging historical stock data to make accurate future price predictions. Tools Required : Programming Language : Python Libraries : TensorFlow or Keras (for building and training the LSTM model) NumPy and Pandas (for data manipulation and preprocessing) Matplotlib or Seaborn (for data visualization) Scikit-learn (for data splitting and evaluation metrics) Development Environment : Jupyter Notebook or any Python IDE Data Sources : Financial APIs (e.g., Alpha Vantage, Yahoo Finance) for historical stock data Hardware : A computer with a good CPU/GPU (optional for faster model training)

7. Algorithm Used: LSTM (Long Short-Term Memory) Neural Network : LSTM is a type of recurrent neural network (RNN) capable of learning long-term dependencies, making it suitable for time-series forecasting like stock price prediction. The LSTM model will process the sequential stock data and learn patterns to predict future stock prices.

8. Importing Libraries and Extracting the Data from CSV

9. Handling the Data

10. STOCK DATA

11. APPLYING LSTM

12. Result

13. References [1] Machine Learning and Data Science Hands on with Python https://www.udemy.com/course/draft/1978034/learn/lecture/12220834#overview [2] Kaggle https://www.kaggle.com/datasets/timoboz/tesla-stock-data-from-2010-to-2020   [3] Solveig Badillo1 , Balazs Banfai1 , Fabian Birzele1 , Iakov I. Davydov1 , Lucy Hutchinson1 , Tony Kam-Thong1 , Juliane Siebourg-Polster1 , Bernhard Steiert1 and Jitao David Zhang https://accio.github.io/AMIDD/assets/2021/06/Badillo-ML-2020.pdf