From 8effe3c94a11604b5a02647d470c3ebd25517dce Mon Sep 17 00:00:00 2001
From: juanis2112 <juanitagomezr2112@gmail.com>
Date: Sun, 23 Feb 2020 17:24:41 -0500
Subject: [PATCH] Adding solutions file

---
 workshop_solutions.py | 122 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 122 insertions(+)
 create mode 100644 workshop_solutions.py

diff --git a/workshop_solutions.py b/workshop_solutions.py
new file mode 100644
index 0000000..e31a981
--- /dev/null
+++ b/workshop_solutions.py
@@ -0,0 +1,122 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright © Spyder Project Contributors
+# Licensed under the terms of the MIT License
+"""Workshop main flow."""
+
+
+# In[1] Importing Libraries and Data
+
+# Third-party imports
+import matplotlib.pyplot as plt
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn import linear_model
+from sklearn.metrics import explained_variance_score
+
+# Local imports
+from utils import (
+    plot_correlations, plot_color_gradients, aggregate_by_year,
+    predicted_temperature)
+
+# In[2] Exploring Data
+
+# Reading data
+weather_data = pd.read_csv('data/weatherHistory.csv')
+
+# Print size of data 
+print(len(weather_data))
+# Print first 3 rows of DataFrame
+print(weather_data.head(3))
+
+# TO DO: Print the last 3 rows of the DataFrame
+print(weather_data.tail(3))
+
+
+# In[3] Visualisation
+
+# Order rows according to date
+weather_data['Formatted Date'] = pd.to_datetime(
+    weather_data['Formatted Date'])
+weather_data_ordered = weather_data.sort_values(by='Formatted Date')
+# Order Index according to date
+weather_data_ordered = weather_data_ordered.reset_index(drop=True)
+# Drop categorical columns
+weather_data_ordered = weather_data_ordered.drop(
+    columns=['Summary', 'Precip Type', 'Loud Cover', 'Daily Summary'])
+# Plot Temperature Vs Formatted Date
+weather_data_ordered.plot(
+    x='Formatted Date', y=['Temperature (C)'], color='red', figsize=(15, 8))
+
+# TO DO: Plot Temperature (C) V.S the Date using only the data from 2006
+weather_data_ordered.head(8759).plot(x='Formatted Date', y=['Temperature (C)'], color='red')
+
+# -----------------------------------------------------------------------------
+# Plot Temperature and Humidity in the same plot
+weather_data_ordered.plot(
+    subplots=True, x='Formatted Date', y=['Temperature (C)', 'Humidity'],
+    figsize=(15, 8))
+# TO DO: Plot different combinations of the variables, for different years
+
+
+# -----------------------------------------------------------------------------
+
+# In[4] Data summarization and aggregation
+
+# Weather data by year
+weather_data_by_year = aggregate_by_year(
+    weather_data_ordered, 'Formatted Date')
+
+# TO DO: Create and use a function to get the average
+#       of the weather data by month
+
+
+# In[5] Data Analysis and Interpretation
+
+# Plot Correlations
+plot_correlations(weather_data_ordered, size=15)
+# Plot Gradients colormaps
+cmap_category, cmap_list = ('Plot gradients convention', ['viridis', ])
+plot_color_gradients(cmap_category, cmap_list)
+
+# Compute Correlations 
+weather_correlations = weather_data_ordered.corr()
+weather_data_ordered['Temperature (C)'].corr(
+    weather_data_ordered['Humidity'])
+
+# TO DO: Get the correlation for different combinations of variables.
+#       Contrast them with the weather_correlations dataframe
+
+
+# In[6] Data Modeling and Prediction
+
+# Get data subsets for the model
+X_train, X_test, Y_train, Y_test = train_test_split(
+    weather_data_ordered['Humidity'], weather_data_ordered['Temperature (C)'],
+    test_size=0.25)
+
+# Run regression
+regresion = linear_model.LinearRegression()
+regresion.fit(X_train.values.reshape(-1, 1), Y_train.values.reshape(-1, 1))
+print(regresion.intercept_, regresion.coef_)  # beta_0=intercept, beta_1=coef_
+
+# Get coefficients
+beta_0 = regresion.intercept_[0]
+beta_1 = regresion.coef_[0, 0]
+
+# Plot predicted model with test data.
+Y_predict = predicted_temperature(X_test, beta_0, beta_1)
+plt.scatter(X_test, Y_test, c='red', label='observation', s=1)
+plt.scatter(X_test, Y_predict, c='blue', label='model')
+plt.xlabel('Humidity')
+plt.ylabel('Temperature (C)')
+plt.legend()
+plt.show()
+
+# TO DO: Using the coefficients predict the temperature for a
+#       given level of humidity using the 'predicted_temperature' function
+#       available in 'utils'
+
+# Evaluate model numerically
+ev = explained_variance_score(Y_test, Y_predict)
+print(ev)