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Using the google stock prices dataset and build a model to predict the future prices using previous instances.

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Stock Price Prediction

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.

Tools Required

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

Roadmap

  1. 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
  1. Report Stock Price Prediction This contains all the qualitative analysis of the results and detailed data visualization.

Real-time stock prediction with the pipeline developed

Prediction

Documentation

Documentation

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Using the google stock prices dataset and build a model to predict the future prices using previous instances.

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