Stock market prediction using greadient boosting

Author: abdoulayegk

machine_learning/stock-market-forecast-using-gradient-boosting-regressor.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# <h1> Problem Statement: Stock Market Analysis and Prediction
+# 
+# Explanation: Our aim is to create software that analyses previous stock data of certain companies,
+# with help of certain parameters that affect stock value. We are going to implement these values in data mining algorithms.
+# This will also help us to determine the values that particular stock will have in near future.
+# We will determine the Month’s High and Low with help of data mining algorithms.
+# In this project we are going to take a five years of stock data for our analysis and prediction
+
+
+#Install the dependencies pip install quandl 
+import quandl
+import numpy as np 
+#plotly.offline.init_notebook_mode(connected=True)
+import plotly.offline as py
+from sklearn.model_selection import train_test_split
+from plotly.offline import iplot, init_notebook_mode
+init_notebook_mode()
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.metrics import r2_score, mean_squared_error
+import matplotlib.pyplot as plt
+
+
+# Get the stock data
+df = quandl.get("WIKI/MSFT")
+# Take a look at the data
+print(df.head())
+
+
+import plotly.express as px 
+fig = px.scatter(df, x="High", y="Low")
+fig.show()
+
+
+# Get the Adjusted Close Price 
+df = df[['Adj. Close']] 
+# Take a look at the new data 
+print(df.head())
+
+
+
+# A variable for predicting 'n' days out into the future
+forecast_out = 30 #'n=30' days
+#Create another column (the target ) shifted 'n' units up
+df['Prediction'] = df[['Adj. Close']].shift(-forecast_out)
+#print the new data set
+print(df.tail())
+
+
+# Convert the dataframe to a numpy array
+X = np.array(df.drop(['Prediction'],1))
+
+#Remove the last '30' rows
+X = X[:-forecast_out]
+print(X)
+
+
+
+### Create the dependent data set (y)  #####
+# Convert the dataframe to a numpy array 
+y = np.array(df['Prediction'])
+# Get all of the y values except the last '30' rows
+y = y[:-forecast_out]
+print(y)
+
+
+x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+
+
+
+params = { 
+    'loss':'ls',
+    'learning_rate':0.1,
+    'n_estimators':500,
+    'min_samples_split':2,
+    'min_weight_fraction_leaf':0.0,
+    'max_depth':3,
+
+}
+model = GradientBoostingRegressor(**params)
+model.fit(x_train,y_train)
+model.score(x_train,y_train).round(3)
+model.score(x_test,y_test).round(3)
+y_pred = model.predict(x_test)
+print('The mean squared error is: ', mean_squared_error(y_test,y_pred))
+print('The variance is: ', r2_score(y_test,y_pred))
+
+# So let's run the model against the test data
+from sklearn.model_selection import cross_val_predict
+
+fig, ax = plt.subplots()
+ax.scatter(y_test, y_pred, edgecolors=(0, 0, 0))
+ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)
+ax.set_xlabel('Actual')
+ax.set_ylabel('Predicted')
+ax.set_title("Ground Truth vs Predicted")
+plt.show()
+# deviance is a goodness-of-fit statistic for a statistical model; it is often used for statistical hypothesis testing. It is a generalization of the idea of using the sum of squares 
+#of residuals in ordinary least squares to cases where model-fitting is achieved by maximum likelihood. 
+test_score = np.zeros((params['n_estimators'],), dtype=np.float64)
+for i, y_pred in enumerate(model.staged_predict(x_test)):
+    test_score[i] = model.loss_(y_test, y_pred)
+
+fig = plt.figure(figsize=(10, 6))
+plt.subplot(1, 1, 1)
+plt.title('Deviance')
+plt.plot(np.arange(params['n_estimators']) + 1, model.train_score_, 'b-',
+         label='Training Set Deviance')
+plt.plot(np.arange(params['n_estimators']) + 1, test_score, 'r-',
+         label='Test Set Deviance')
+plt.legend(loc='upper right')
+plt.xlabel('Boosting Iterations')
+plt.ylabel('Deviance')
+fig.tight_layout()
+plt.show()
+
+
+
+
+