Using the google stock prices dataset and build a model to predict the future prices using previous instances. A pipeline is also developed through which allows continuous predictions taking the present data itself and generating the future estimated stock prices.
Scikit Learn: ML Library used
Matplotlib: Data Visualization
Tensorflow: Deep Learning Models
Pandas: Python data manipulation libraries
Numpy: Working with data in form of arrays
- Stock Price Prediction.ipynb This is the main file with all the preprocessing, various Machine learning, Deep Learning Models and a real-time Pipeline.
- Installing libraries and dependency
- Importing the dataset - Google Stock Price Prediction Dataset
- Exploratory Data Analysis and Visualisation
- Data Preprocessing - Basic preprocessing and structuring the dataset
- Dividing the dataset into train and test
- Applying Machine Learning models
- Linear Regression
- Random Forest Regressor
- Light GBM Regressor
- XG Boost Regressor
- Applying Deep Learning models
- Pipeline developed with the best model for future predictions with the real time data
- Report Stock Price Prediction This contains all the qualitative analysis of the results and detailed data visualization.
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