This project aims to predict stock prices using Scikit-learn library and plotting a graph using Matplotlib. We create three models for this using SVR(Support Vector Regression) from sklearn(since support vector machine can be used in classification as well a regression). The three models are linear, polynomial and rbf(radio basis function) and then predict which model gives the best result.
Language: Python 3.8
Libraries and Modules: Numpy, Matplotlib, Keras, scikit-learn, pandas
Dataset: Last 3 months Data from NASDAQ
Keywords: Classification, tpot, radiation, machine learning
Step 1: Get Data from NASDAQ on the choice of company and return Pandas framework.
Step 2: Load the data into sequence with the seq length.
Step 3: Build a 2 stacked LSTM with 2 FCL with Keras, and return a Keras.Sequential.
Step 4: Train the model and tune hyperparameters.
Step 5: Test the model.
Step 6: Plot the graph of growth using Matplotlib.
Note: This is an updated version of preferred procedure of the pervious code.
Results
References