diff --git a/assets/starter-notebook.ipynb b/assets/starter-notebook.ipynb new file mode 100644 index 00000000..e79ebd14 --- /dev/null +++ b/assets/starter-notebook.ipynb @@ -0,0 +1,26279 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2021-06-11T09:24:53.643384Z", + "start_time": "2021-06-11T09:24:53.622385Z" + } + }, + "source": [ + "# Regression Predict Student Solution\n", + "\n", + "© Explore Data Science Academy\n", + "\n", + "---\n", + "### Honour Code\n", + "\n", + "I {**Advance Regression, TEAM NM4**}, confirm - by submitting this document - that the solutions in this notebook are a result of my own work and that I abide by the [EDSA honour code](https://drive.google.com/file/d/1QDCjGZJ8-FmJE3bZdIQNwnJyQKPhHZBn/view?usp=sharing).\n", + "\n", + "Non-compliance with the honour code constitutes a material breach of contract.\n", + "\n", + "### Predict Overview: Spain Electricity Shortfall Challenge\n", + "\n", + "The government of Spain is considering an expansion of it's renewable energy resource infrastructure investments. As such, they require information on the trends and patterns of the countries renewable sources and fossil fuel energy generation. Your company has been awarded the contract to:\n", + "\n", + "- 1. analyse the supplied data;\n", + "- 2. identify potential errors in the data and clean the existing data set;\n", + "- 3. determine if additional features can be added to enrich the data set;\n", + "- 4. build a model that is capable of forecasting the three hourly demand shortfalls;\n", + "- 5. evaluate the accuracy of the best machine learning model;\n", + "- 6. determine what features were most important in the model’s prediction decision, and\n", + "- 7. explain the inner working of the model to a non-technical audience.\n", + "\n", + "Formally the problem statement was given to you, the senior data scientist, by your manager via email reads as follow:\n", + "\n", + "> In this project you are tasked to model the shortfall between the energy generated by means of fossil fuels and various renewable sources - for the country of Spain. The daily shortfall, which will be referred to as the target variable, will be modelled as a function of various city-specific weather features such as `pressure`, `wind speed`, `humidity`, etc. As with all data science projects, the provided features are rarely adequate predictors of the target variable. As such, you are required to perform feature engineering to ensure that you will be able to accurately model Spain's three hourly shortfalls.\n", + " \n", + "On top of this, she has provided you with a starter notebook containing vague explanations of what the main outcomes are. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Table of Contents\n", + "\n", + "1. Importing Packages\n", + "\n", + "2. Loading Data\n", + "\n", + "3. Exploratory Data Analysis (EDA)\n", + "\n", + "4. Data Engineering\n", + "\n", + "5. Modeling\n", + "\n", + "6. Model Performance\n", + "\n", + "7. Model Explanations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "## 1. Importing Packages\n", + "Back to Table of Contents\n", + "\n", + "---\n", + " \n", + "| ⚡ Description: Importing Packages ⚡ |\n", + "| :--------------------------- |\n", + "| In this section you are required to import, and briefly discuss, the libraries that will be used throughout your analysis and modelling. |\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: plotly in c:\\users\\osas.h\\anaconda3\\lib\\site-packages (4.14.3)\n", + "Requirement already satisfied: retrying>=1.3.3 in c:\\users\\osas.h\\anaconda3\\lib\\site-packages (from plotly) (1.3.3)\n", + "Requirement already satisfied: six in c:\\users\\osas.h\\anaconda3\\lib\\site-packages (from plotly) (1.14.0)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install plotly" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2021-06-23T10:30:53.800892Z", + "start_time": "2021-06-23T10:30:50.215449Z" + } + }, + "outputs": [], + "source": [ + "# Libraries for data loading, data manipulation and data visulisation\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import rc\n", + "import seaborn as sns\n", + "import plotly.express as px\n", + "# Libraries for data preparation and model building\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn import metrics \n", + "from sklearn.tree import DecisionTreeRegressor\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import chi2\n", + "from sklearn.metrics import mean_squared_error as MSE\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.metrics import r2_score\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.svm import SVR\n", + "from sklearn.linear_model import LinearRegression\n", + "import math\n", + "from statsmodels.graphics.correlation import plot_corr\n", + "import statsmodels.formula.api as sm\n", + "from statsmodels.formula.api import ols\n", + "from scipy.stats import pearsonr\n", + "from sklearn.linear_model import Lasso\n", + "from sklearn.linear_model import Ridge\n", + "\n", + "\n", + "# Setting global constants to ensure notebook results are reproducible\n", + "#PARAMETER_CONSTANT = ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 2. Loading the Data\n", + "\n", + "Back to Table of Contents\n", + "\n", + "---\n", + " \n", + "| ⚡ Description: Loading the data ⚡ |\n", + "| :--------------------------- |\n", + "| In this section you are required to load the data from the `df_train` file into a DataFrame. |\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2021-06-28T08:49:35.311495Z", + "start_time": "2021-06-28T08:49:35.295494Z" + } + }, + "outputs": [], + "source": [ + "df = pd.read_csv('df_train.csv')\n", + "df_test= pd.read_csv('df_test.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2920, 48)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_test.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "df_test.isnull().sum()\n", + "df_test['Valencia_pressure'].fillna(df['Valencia_pressure'].mean(), inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Unnamed: 0 0\n", + "time 0\n", + "Madrid_wind_speed 0\n", + "Valencia_wind_deg 0\n", + "Bilbao_rain_1h 0\n", + "Valencia_wind_speed 0\n", + "Seville_humidity 0\n", + "Madrid_humidity 0\n", + "Bilbao_clouds_all 0\n", + "Bilbao_wind_speed 0\n", + "Seville_clouds_all 0\n", + "Bilbao_wind_deg 0\n", + "Barcelona_wind_speed 0\n", + "Barcelona_wind_deg 0\n", + "Madrid_clouds_all 0\n", + "Seville_wind_speed 0\n", + "Barcelona_rain_1h 0\n", + "Seville_pressure 0\n", + "Seville_rain_1h 0\n", + "Bilbao_snow_3h 0\n", + "Barcelona_pressure 0\n", + "Seville_rain_3h 0\n", + "Madrid_rain_1h 0\n", + "Barcelona_rain_3h 0\n", + "Valencia_snow_3h 0\n", + "Madrid_weather_id 0\n", + "Barcelona_weather_id 0\n", + "Bilbao_pressure 0\n", + "Seville_weather_id 0\n", + "Valencia_pressure 0\n", + "Seville_temp_max 0\n", + "Madrid_pressure 0\n", + "Valencia_temp_max 0\n", + "Valencia_temp 0\n", + "Bilbao_weather_id 0\n", + "Seville_temp 0\n", + "Valencia_humidity 0\n", + "Valencia_temp_min 0\n", + "Barcelona_temp_max 0\n", + "Madrid_temp_max 0\n", + "Barcelona_temp 0\n", + "Bilbao_temp_min 0\n", + "Bilbao_temp 0\n", + "Barcelona_temp_min 0\n", + "Bilbao_temp_max 0\n", + "Seville_temp_min 0\n", + "Madrid_temp 0\n", + "Madrid_temp_min 0\n", + "dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_test.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 3. Exploratory Data Analysis (EDA)\n", + "\n", + "Back to Table of Contents\n", + "\n", + "---\n", + " \n", + "| ⚡ Description: Exploratory data analysis ⚡ |\n", + "| :--------------------------- |\n", + "| In this section, you are required to perform an in-depth analysis of all the variables in the DataFrame. |\n", + "\n", + "---\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2021-06-28T08:52:37.824204Z", + "start_time": "2021-06-28T08:52:37.811206Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " There are 8763 rows and 49 columns\n" + ] + }, + { + "data": { + "text/html": [ + "
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Unnamed: 0timeMadrid_wind_speedValencia_wind_degBilbao_rain_1hValencia_wind_speedSeville_humidityMadrid_humidityBilbao_clouds_allBilbao_wind_speed...Madrid_temp_maxBarcelona_tempBilbao_temp_minBilbao_tempBarcelona_temp_minBilbao_temp_maxSeville_temp_minMadrid_tempMadrid_temp_minload_shortfall_3h
002015-01-01 03:00:000.666667level_50.00.66666774.33333364.0000000.01.000000...265.938000281.013000269.338615269.338615281.013000269.338615274.254667265.938000265.9380006715.666667
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time2015-01-01 03:00:002015-01-01 06:00:002015-01-01 09:00:002015-01-01 12:00:002015-01-01 15:00:002015-01-01 18:00:002015-01-01 21:00:002015-01-02 00:00:002015-01-02 03:00:002015-01-02 06:00:00
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Bilbao_pressure10351035.67103610361035.331035.67103610361036.671037
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Valencia_temp269.888271.728278.009284.9283.015277.623272.27271.041270.535271.661
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Valencia_humidity75.66677165.66675458.333372.666783.33338280.666775.3333
Valencia_temp_min269.888271.728278.009284.9283.015277.623272.27271.041270.535271.661
Barcelona_temp_max281.013280.562281.584283.434284.213284.166283.42282.474281.727281.803
Madrid_temp_max265.938266.387272.709281.895280.678274.639268.287266.882266.227266.878
Barcelona_temp281.013280.562281.584283.434284.213284.166283.42282.474281.727281.803
Bilbao_temp_min269.339270.376275.027281.135282.252277.919274.295272.903271.78271.674
Bilbao_temp269.339270.376275.027281.135282.252277.919274.295272.903271.78271.674
Barcelona_temp_min281.013280.562281.584283.434284.213284.166283.42282.474281.727281.803
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Seville_temp_min274.255274.945278.792285.394285.514282.078277.637276.457276.152276.453
Madrid_temp265.938266.387272.709281.895280.678274.639268.287266.882266.227266.878
Madrid_temp_min265.938266.387272.709281.895280.678274.639268.287266.882266.227266.878
load_shortfall_3h6715.674171.674274.675075.676620.67684210760.310866-1850.33-4002.33
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\n", + "Seville_clouds_all 0 0 \n", + "Bilbao_wind_deg 185 191.333 \n", + "Barcelona_wind_speed 4.33333 4.66667 \n", + "Barcelona_wind_deg 260 254.667 \n", + "Madrid_clouds_all 0 0 \n", + "Seville_wind_speed 3 2.66667 \n", + "Barcelona_rain_1h 0 0 \n", + "Seville_pressure sp25 sp25 \n", + "Seville_rain_1h 0 0 \n", + "Bilbao_snow_3h 0 0 \n", + "Barcelona_pressure 1035 1035.67 \n", + "Seville_rain_3h 0 0 \n", + "Madrid_rain_1h 0 0 \n", + "Barcelona_rain_3h 0 0 \n", + "Valencia_snow_3h 0 0 \n", + "Madrid_weather_id 800 800 \n", + "Barcelona_weather_id 800 800 \n", + "Bilbao_pressure 1035.33 1035.67 \n", + "Seville_weather_id 800 800 \n", + "Valencia_pressure NaN NaN \n", + "Seville_temp_max 285.514 282.078 \n", + "Madrid_pressure 1035.33 1013.33 \n", + "Valencia_temp_max 283.015 277.623 \n", + "Valencia_temp 283.015 277.623 \n", + "Bilbao_weather_id 800 800.667 \n", + "Seville_temp 285.514 282.078 \n", + "Valencia_humidity 58.3333 72.6667 \n", + "Valencia_temp_min 283.015 277.623 \n", + "Barcelona_temp_max 284.213 284.166 \n", + "Madrid_temp_max 280.678 274.639 \n", + "Barcelona_temp 284.213 284.166 \n", + "Bilbao_temp_min 282.252 277.919 \n", + "Bilbao_temp 282.252 277.919 \n", + "Barcelona_temp_min 284.213 284.166 \n", + "Bilbao_temp_max 282.252 277.919 \n", + "Seville_temp_min 285.514 282.078 \n", + "Madrid_temp 280.678 274.639 \n", + "Madrid_temp_min 280.678 274.639 \n", + "load_shortfall_3h 6620.67 6842 \n", + "\n", + " 6 7 \\\n", + "Unnamed: 0 6 7 \n", + "time 2015-01-01 21:00:00 2015-01-02 00:00:00 \n", + "Madrid_wind_speed 1 1 \n", + "Valencia_wind_deg level_8 level_9 \n", + "Bilbao_rain_1h 0 0 \n", + "Valencia_wind_speed 1 1 \n", + "Seville_humidity 83.6667 83.6667 \n", + "Madrid_humidity 63.3333 64 \n", + "Bilbao_clouds_all 16.3333 8.66667 \n", + "Bilbao_wind_speed 1 1.33333 \n", + "Seville_clouds_all 0 0 \n", + "Bilbao_wind_deg 210.333 238.667 \n", + "Barcelona_wind_speed 3.33333 2 \n", + "Barcelona_wind_deg 276 310 \n", + "Madrid_clouds_all 0 0 \n", + "Seville_wind_speed 2 2.33333 \n", + "Barcelona_rain_1h 0 0 \n", + "Seville_pressure sp25 sp25 \n", + "Seville_rain_1h 0 0 \n", + "Bilbao_snow_3h 0 0 \n", + "Barcelona_pressure 1038 1037.67 \n", + "Seville_rain_3h 0 0 \n", + "Madrid_rain_1h 0 0 \n", + "Barcelona_rain_3h 0 0 \n", + "Valencia_snow_3h 0 0 \n", + "Madrid_weather_id 800 800 \n", + "Barcelona_weather_id 800 800 \n", + "Bilbao_pressure 1036 1036 \n", + "Seville_weather_id 800 800 \n", + "Valencia_pressure 1006 1005.67 \n", + "Seville_temp_max 277.637 276.457 \n", + "Madrid_pressure 974.333 974.667 \n", + "Valencia_temp_max 272.27 271.041 \n", + "Valencia_temp 272.27 271.041 \n", + "Bilbao_weather_id 800.667 800.333 \n", + "Seville_temp 277.637 276.457 \n", + "Valencia_humidity 83.3333 82 \n", + "Valencia_temp_min 272.27 271.041 \n", + "Barcelona_temp_max 283.42 282.474 \n", + "Madrid_temp_max 268.287 266.882 \n", + "Barcelona_temp 283.42 282.474 \n", + "Bilbao_temp_min 274.295 272.903 \n", + "Bilbao_temp 274.295 272.903 \n", + "Barcelona_temp_min 283.42 282.474 \n", + "Bilbao_temp_max 274.295 272.903 \n", + "Seville_temp_min 277.637 276.457 \n", + "Madrid_temp 268.287 266.882 \n", + "Madrid_temp_min 268.287 266.882 \n", + "load_shortfall_3h 10760.3 10866 \n", + "\n", + " 8 9 \n", + "Unnamed: 0 8 9 \n", + "time 2015-01-02 03:00:00 2015-01-02 06:00:00 \n", + "Madrid_wind_speed 1 1 \n", + "Valencia_wind_deg level_9 level_9 \n", + "Bilbao_rain_1h 0 0 \n", + "Valencia_wind_speed 1 1.33333 \n", + "Seville_humidity 86 87 \n", + "Madrid_humidity 63.3333 63.6667 \n", + "Bilbao_clouds_all 5.33333 15.3333 \n", + "Bilbao_wind_speed 1 1 \n", + "Seville_clouds_all 0 0 \n", + "Bilbao_wind_deg 258.333 255.333 \n", + "Barcelona_wind_speed 1.66667 1 \n", + "Barcelona_wind_deg 338 329 \n", + "Madrid_clouds_all 0 0 \n", + "Seville_wind_speed 3 3 \n", + "Barcelona_rain_1h 0 0 \n", + "Seville_pressure sp25 sp25 \n", + "Seville_rain_1h 0 0 \n", + "Bilbao_snow_3h 0 0 \n", + "Barcelona_pressure 1037.33 1038.33 \n", + "Seville_rain_3h 0 0 \n", + "Madrid_rain_1h 0 0 \n", + "Barcelona_rain_3h 0 0 \n", + "Valencia_snow_3h 0 0 \n", + "Madrid_weather_id 800 800 \n", + "Barcelona_weather_id 800 800 \n", + "Bilbao_pressure 1036.67 1037 \n", + "Seville_weather_id 800 800 \n", + "Valencia_pressure 1005.33 1006.33 \n", + "Seville_temp_max 276.152 276.453 \n", + "Madrid_pressure 974.333 975.333 \n", + "Valencia_temp_max 270.535 271.661 \n", + "Valencia_temp 270.535 271.661 \n", + "Bilbao_weather_id 800 801 \n", + "Seville_temp 276.152 276.453 \n", + "Valencia_humidity 80.6667 75.3333 \n", + "Valencia_temp_min 270.535 271.661 \n", + "Barcelona_temp_max 281.727 281.803 \n", + "Madrid_temp_max 266.227 266.878 \n", + "Barcelona_temp 281.727 281.803 \n", + "Bilbao_temp_min 271.78 271.674 \n", + "Bilbao_temp 271.78 271.674 \n", + "Barcelona_temp_min 281.727 281.803 \n", + "Bilbao_temp_max 271.78 271.674 \n", + "Seville_temp_min 276.152 276.453 \n", + "Madrid_temp 266.227 266.878 \n", + "Madrid_temp_min 266.227 266.878 \n", + "load_shortfall_3h -1850.33 -4002.33 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(10).T" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 8763 entries, 0 to 8762\n", + "Data columns (total 49 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Unnamed: 0 8763 non-null int64 \n", + " 1 time 8763 non-null object \n", + " 2 Madrid_wind_speed 8763 non-null float64\n", + " 3 Valencia_wind_deg 8763 non-null object \n", + " 4 Bilbao_rain_1h 8763 non-null float64\n", + " 5 Valencia_wind_speed 8763 non-null float64\n", + " 6 Seville_humidity 8763 non-null float64\n", + " 7 Madrid_humidity 8763 non-null float64\n", + " 8 Bilbao_clouds_all 8763 non-null float64\n", + " 9 Bilbao_wind_speed 8763 non-null float64\n", + " 10 Seville_clouds_all 8763 non-null float64\n", + " 11 Bilbao_wind_deg 8763 non-null float64\n", + " 12 Barcelona_wind_speed 8763 non-null float64\n", + " 13 Barcelona_wind_deg 8763 non-null float64\n", + " 14 Madrid_clouds_all 8763 non-null float64\n", + " 15 Seville_wind_speed 8763 non-null float64\n", + " 16 Barcelona_rain_1h 8763 non-null float64\n", + " 17 Seville_pressure 8763 non-null object \n", + " 18 Seville_rain_1h 8763 non-null float64\n", + " 19 Bilbao_snow_3h 8763 non-null float64\n", + " 20 Barcelona_pressure 8763 non-null float64\n", + " 21 Seville_rain_3h 8763 non-null float64\n", + " 22 Madrid_rain_1h 8763 non-null float64\n", + " 23 Barcelona_rain_3h 8763 non-null float64\n", + " 24 Valencia_snow_3h 8763 non-null float64\n", + " 25 Madrid_weather_id 8763 non-null float64\n", + " 26 Barcelona_weather_id 8763 non-null float64\n", + " 27 Bilbao_pressure 8763 non-null float64\n", + " 28 Seville_weather_id 8763 non-null float64\n", + " 29 Valencia_pressure 6695 non-null float64\n", + " 30 Seville_temp_max 8763 non-null float64\n", + " 31 Madrid_pressure 8763 non-null float64\n", + " 32 Valencia_temp_max 8763 non-null float64\n", + " 33 Valencia_temp 8763 non-null float64\n", + " 34 Bilbao_weather_id 8763 non-null float64\n", + " 35 Seville_temp 8763 non-null float64\n", + " 36 Valencia_humidity 8763 non-null float64\n", + " 37 Valencia_temp_min 8763 non-null float64\n", + " 38 Barcelona_temp_max 8763 non-null float64\n", + " 39 Madrid_temp_max 8763 non-null float64\n", + " 40 Barcelona_temp 8763 non-null float64\n", + " 41 Bilbao_temp_min 8763 non-null float64\n", + " 42 Bilbao_temp 8763 non-null float64\n", + " 43 Barcelona_temp_min 8763 non-null float64\n", + " 44 Bilbao_temp_max 8763 non-null float64\n", + " 45 Seville_temp_min 8763 non-null float64\n", + " 46 Madrid_temp 8763 non-null float64\n", + " 47 Madrid_temp_min 8763 non-null float64\n", + " 48 load_shortfall_3h 8763 non-null float64\n", + "dtypes: float64(45), int64(1), object(3)\n", + "memory usage: 3.3+ MB\n", + "None\n" + ] + } + ], + "source": [ + "print(df.info()) " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unnamed: 0 0\n", + "time 0\n", + "Madrid_wind_speed 0\n", + "Valencia_wind_deg 0\n", + "Bilbao_rain_1h 0\n", + "Valencia_wind_speed 0\n", + "Seville_humidity 0\n", + "Madrid_humidity 0\n", + "Bilbao_clouds_all 0\n", + "Bilbao_wind_speed 0\n", + "Seville_clouds_all 0\n", + "Bilbao_wind_deg 0\n", + "Barcelona_wind_speed 0\n", + "Barcelona_wind_deg 0\n", + "Madrid_clouds_all 0\n", + "Seville_wind_speed 0\n", + "Barcelona_rain_1h 0\n", + "Seville_pressure 0\n", + "Seville_rain_1h 0\n", + "Bilbao_snow_3h 0\n", + "Barcelona_pressure 0\n", + "Seville_rain_3h 0\n", + "Madrid_rain_1h 0\n", + "Barcelona_rain_3h 0\n", + "Valencia_snow_3h 0\n", + "Madrid_weather_id 0\n", + "Barcelona_weather_id 0\n", + "Bilbao_pressure 0\n", + "Seville_weather_id 0\n", + "Valencia_pressure 2068\n", + "Seville_temp_max 0\n", + "Madrid_pressure 0\n", + "Valencia_temp_max 0\n", + "Valencia_temp 0\n", + "Bilbao_weather_id 0\n", + "Seville_temp 0\n", + "Valencia_humidity 0\n", + "Valencia_temp_min 0\n", + "Barcelona_temp_max 0\n", + "Madrid_temp_max 0\n", + "Barcelona_temp 0\n", + "Bilbao_temp_min 0\n", + "Bilbao_temp 0\n", + "Barcelona_temp_min 0\n", + "Bilbao_temp_max 0\n", + "Seville_temp_min 0\n", + "Madrid_temp 0\n", + "Madrid_temp_min 0\n", + "load_shortfall_3h 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "print(df.isnull().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countmeanstdmin25%50%75%max
Unnamed: 08763.04381.0000002529.8045380.0000002190.5000004381.0000006571.5000008.762000e+03
Madrid_wind_speed8763.02.4257291.8503710.0000001.0000002.0000003.3333331.300000e+01
Bilbao_rain_1h8763.00.1357530.3749010.0000000.0000000.0000000.1000003.000000e+00
Valencia_wind_speed8763.02.5862722.4111900.0000001.0000001.6666673.6666675.200000e+01
Seville_humidity8763.062.65879322.6212268.33333344.33333365.66666782.0000001.000000e+02
Madrid_humidity8763.057.41471724.3353966.33333336.33333358.00000078.6666671.000000e+02
Bilbao_clouds_all8763.043.46913232.5510440.00000010.00000045.00000075.0000001.000000e+02
Bilbao_wind_speed8763.01.8503561.6958880.0000000.6666671.0000002.6666671.266667e+01
Seville_clouds_all8763.013.71474824.2724820.0000000.0000000.00000020.0000009.733333e+01
Bilbao_wind_deg8763.0158.957511102.0562990.00000073.333333147.000000234.0000003.593333e+02
Barcelona_wind_speed8763.02.8704971.7921970.0000001.6666672.6666674.0000001.266667e+01
Barcelona_wind_deg8763.0190.54484889.0773370.000000118.166667200.000000260.0000003.600000e+02
Madrid_clouds_all8763.019.47339228.0536600.0000000.0000000.00000033.3333331.000000e+02
Seville_wind_speed8763.02.4250451.6728950.0000001.0000002.0000003.3333331.166667e+01
Barcelona_rain_1h8763.00.1289060.6347300.0000000.0000000.0000000.0000001.200000e+01
Seville_rain_1h8763.00.0394390.1758570.0000000.0000000.0000000.0000003.000000e+00
Bilbao_snow_3h8763.00.0319120.5572640.0000000.0000000.0000000.0000002.130000e+01
Barcelona_pressure8763.01377.96460514073.140990670.6666671014.0000001018.0000001022.0000001.001411e+06
Seville_rain_3h8763.00.0002430.0036600.0000000.0000000.0000000.0000009.333333e-02
Madrid_rain_1h8763.00.0378180.1526390.0000000.0000000.0000000.0000003.000000e+00
Barcelona_rain_3h8763.00.0004390.0039940.0000000.0000000.0000000.0000009.300000e-02
Valencia_snow_3h8763.00.0002050.0118660.0000000.0000000.0000000.0000007.916667e-01
Madrid_weather_id8763.0773.52759477.313315211.000000800.000000800.000000800.6666678.040000e+02
Barcelona_weather_id8763.0765.97968788.142235200.666667800.000000800.333333801.0000008.040000e+02
Bilbao_pressure8763.01017.73954910.046124971.3333331013.0000001019.0000001024.0000001.042000e+03
Seville_weather_id8763.0774.65881871.940009200.000000800.000000800.000000800.0000008.040000e+02
Valencia_pressure6695.01012.0514079.506214972.6666671010.3333331015.0000001018.0000001.021667e+03
Seville_temp_max8763.0297.4795278.875812272.063000291.312750297.101667304.1500003.204833e+02
Madrid_pressure8763.01010.31692022.198555927.6666671012.3333331017.3333331022.0000001.038000e+03
Valencia_temp_max8763.0291.3372337.565692269.888000285.550167291.037000297.2483333.142633e+02
Valencia_temp8763.0290.5921527.162274269.888000285.150000290.176667296.0566673.104267e+02
Bilbao_weather_id8763.0724.722362115.846537207.333333700.333333800.000000801.6666678.040000e+02
Seville_temp8763.0293.9789037.920986272.063000288.282917293.323333299.6203333.149767e+02
Valencia_humidity8763.065.24772719.26232210.33333351.33333367.00000081.3333331.000000e+02
Valencia_temp_min8763.0289.8676486.907402269.888000284.783333289.550000294.8200003.102720e+02
Barcelona_temp_max8763.0291.1576447.273538272.150000285.483333290.150000296.8550003.140767e+02
Madrid_temp_max8763.0289.5403099.752047264.983333282.150000288.116177296.8166673.144833e+02
Barcelona_temp8763.0289.8554596.528111270.816667284.973443289.416667294.9090003.073167e+02
Bilbao_temp_min8763.0285.0179736.705672264.483333280.085167284.816667289.8166673.098167e+02
Bilbao_temp8763.0286.4229296.818682267.483333281.374167286.158333291.0341673.107100e+02
Barcelona_temp_min8763.0288.4474226.102593269.483333284.150000288.150000292.9666673.048167e+02
Bilbao_temp_max8763.0287.9660277.105590269.063000282.836776287.630000292.4833333.179667e+02
Seville_temp_min8763.0291.6333568.178220270.150000285.816667290.816667297.1500003.148167e+02
Madrid_temp8763.0288.4194399.346796264.983333281.404281287.053333295.1546673.131333e+02
Madrid_temp_min8763.0287.2022039.206237264.983333280.299167286.083333293.8845003.103833e+02
load_shortfall_3h8763.010673.8576125218.046404-6618.0000007390.33333311114.66666714498.1666673.190400e+04
\n", + "
" + ], + "text/plain": [ + " count mean std min \\\n", + "Unnamed: 0 8763.0 4381.000000 2529.804538 0.000000 \n", + "Madrid_wind_speed 8763.0 2.425729 1.850371 0.000000 \n", + "Bilbao_rain_1h 8763.0 0.135753 0.374901 0.000000 \n", + "Valencia_wind_speed 8763.0 2.586272 2.411190 0.000000 \n", + "Seville_humidity 8763.0 62.658793 22.621226 8.333333 \n", + "Madrid_humidity 8763.0 57.414717 24.335396 6.333333 \n", + "Bilbao_clouds_all 8763.0 43.469132 32.551044 0.000000 \n", + "Bilbao_wind_speed 8763.0 1.850356 1.695888 0.000000 \n", + "Seville_clouds_all 8763.0 13.714748 24.272482 0.000000 \n", + "Bilbao_wind_deg 8763.0 158.957511 102.056299 0.000000 \n", + "Barcelona_wind_speed 8763.0 2.870497 1.792197 0.000000 \n", + "Barcelona_wind_deg 8763.0 190.544848 89.077337 0.000000 \n", + "Madrid_clouds_all 8763.0 19.473392 28.053660 0.000000 \n", + "Seville_wind_speed 8763.0 2.425045 1.672895 0.000000 \n", + "Barcelona_rain_1h 8763.0 0.128906 0.634730 0.000000 \n", + "Seville_rain_1h 8763.0 0.039439 0.175857 0.000000 \n", + "Bilbao_snow_3h 8763.0 0.031912 0.557264 0.000000 \n", + "Barcelona_pressure 8763.0 1377.964605 14073.140990 670.666667 \n", + "Seville_rain_3h 8763.0 0.000243 0.003660 0.000000 \n", + "Madrid_rain_1h 8763.0 0.037818 0.152639 0.000000 \n", + "Barcelona_rain_3h 8763.0 0.000439 0.003994 0.000000 \n", + "Valencia_snow_3h 8763.0 0.000205 0.011866 0.000000 \n", + "Madrid_weather_id 8763.0 773.527594 77.313315 211.000000 \n", + "Barcelona_weather_id 8763.0 765.979687 88.142235 200.666667 \n", + "Bilbao_pressure 8763.0 1017.739549 10.046124 971.333333 \n", + "Seville_weather_id 8763.0 774.658818 71.940009 200.000000 \n", + "Valencia_pressure 6695.0 1012.051407 9.506214 972.666667 \n", + "Seville_temp_max 8763.0 297.479527 8.875812 272.063000 \n", + "Madrid_pressure 8763.0 1010.316920 22.198555 927.666667 \n", + "Valencia_temp_max 8763.0 291.337233 7.565692 269.888000 \n", + "Valencia_temp 8763.0 290.592152 7.162274 269.888000 \n", + "Bilbao_weather_id 8763.0 724.722362 115.846537 207.333333 \n", + "Seville_temp 8763.0 293.978903 7.920986 272.063000 \n", + "Valencia_humidity 8763.0 65.247727 19.262322 10.333333 \n", + "Valencia_temp_min 8763.0 289.867648 6.907402 269.888000 \n", + "Barcelona_temp_max 8763.0 291.157644 7.273538 272.150000 \n", + "Madrid_temp_max 8763.0 289.540309 9.752047 264.983333 \n", + "Barcelona_temp 8763.0 289.855459 6.528111 270.816667 \n", + "Bilbao_temp_min 8763.0 285.017973 6.705672 264.483333 \n", + "Bilbao_temp 8763.0 286.422929 6.818682 267.483333 \n", + "Barcelona_temp_min 8763.0 288.447422 6.102593 269.483333 \n", + "Bilbao_temp_max 8763.0 287.966027 7.105590 269.063000 \n", + "Seville_temp_min 8763.0 291.633356 8.178220 270.150000 \n", + "Madrid_temp 8763.0 288.419439 9.346796 264.983333 \n", + "Madrid_temp_min 8763.0 287.202203 9.206237 264.983333 \n", + "load_shortfall_3h 8763.0 10673.857612 5218.046404 -6618.000000 \n", + "\n", + " 25% 50% 75% max \n", + "Unnamed: 0 2190.500000 4381.000000 6571.500000 8.762000e+03 \n", + "Madrid_wind_speed 1.000000 2.000000 3.333333 1.300000e+01 \n", + "Bilbao_rain_1h 0.000000 0.000000 0.100000 3.000000e+00 \n", + "Valencia_wind_speed 1.000000 1.666667 3.666667 5.200000e+01 \n", + "Seville_humidity 44.333333 65.666667 82.000000 1.000000e+02 \n", + "Madrid_humidity 36.333333 58.000000 78.666667 1.000000e+02 \n", + "Bilbao_clouds_all 10.000000 45.000000 75.000000 1.000000e+02 \n", + "Bilbao_wind_speed 0.666667 1.000000 2.666667 1.266667e+01 \n", + "Seville_clouds_all 0.000000 0.000000 20.000000 9.733333e+01 \n", + "Bilbao_wind_deg 73.333333 147.000000 234.000000 3.593333e+02 \n", + "Barcelona_wind_speed 1.666667 2.666667 4.000000 1.266667e+01 \n", + "Barcelona_wind_deg 118.166667 200.000000 260.000000 3.600000e+02 \n", + "Madrid_clouds_all 0.000000 0.000000 33.333333 1.000000e+02 \n", + "Seville_wind_speed 1.000000 2.000000 3.333333 1.166667e+01 \n", + "Barcelona_rain_1h 0.000000 0.000000 0.000000 1.200000e+01 \n", + "Seville_rain_1h 0.000000 0.000000 0.000000 3.000000e+00 \n", + "Bilbao_snow_3h 0.000000 0.000000 0.000000 2.130000e+01 \n", + "Barcelona_pressure 1014.000000 1018.000000 1022.000000 1.001411e+06 \n", + "Seville_rain_3h 0.000000 0.000000 0.000000 9.333333e-02 \n", + "Madrid_rain_1h 0.000000 0.000000 0.000000 3.000000e+00 \n", + "Barcelona_rain_3h 0.000000 0.000000 0.000000 9.300000e-02 \n", + "Valencia_snow_3h 0.000000 0.000000 0.000000 7.916667e-01 \n", + "Madrid_weather_id 800.000000 800.000000 800.666667 8.040000e+02 \n", + "Barcelona_weather_id 800.000000 800.333333 801.000000 8.040000e+02 \n", + "Bilbao_pressure 1013.000000 1019.000000 1024.000000 1.042000e+03 \n", + "Seville_weather_id 800.000000 800.000000 800.000000 8.040000e+02 \n", + "Valencia_pressure 1010.333333 1015.000000 1018.000000 1.021667e+03 \n", + "Seville_temp_max 291.312750 297.101667 304.150000 3.204833e+02 \n", + "Madrid_pressure 1012.333333 1017.333333 1022.000000 1.038000e+03 \n", + "Valencia_temp_max 285.550167 291.037000 297.248333 3.142633e+02 \n", + "Valencia_temp 285.150000 290.176667 296.056667 3.104267e+02 \n", + "Bilbao_weather_id 700.333333 800.000000 801.666667 8.040000e+02 \n", + "Seville_temp 288.282917 293.323333 299.620333 3.149767e+02 \n", + "Valencia_humidity 51.333333 67.000000 81.333333 1.000000e+02 \n", + "Valencia_temp_min 284.783333 289.550000 294.820000 3.102720e+02 \n", + "Barcelona_temp_max 285.483333 290.150000 296.855000 3.140767e+02 \n", + "Madrid_temp_max 282.150000 288.116177 296.816667 3.144833e+02 \n", + "Barcelona_temp 284.973443 289.416667 294.909000 3.073167e+02 \n", + "Bilbao_temp_min 280.085167 284.816667 289.816667 3.098167e+02 \n", + "Bilbao_temp 281.374167 286.158333 291.034167 3.107100e+02 \n", + "Barcelona_temp_min 284.150000 288.150000 292.966667 3.048167e+02 \n", + "Bilbao_temp_max 282.836776 287.630000 292.483333 3.179667e+02 \n", + "Seville_temp_min 285.816667 290.816667 297.150000 3.148167e+02 \n", + "Madrid_temp 281.404281 287.053333 295.154667 3.131333e+02 \n", + "Madrid_temp_min 280.299167 286.083333 293.884500 3.103833e+02 \n", + "load_shortfall_3h 7390.333333 11114.666667 14498.166667 3.190400e+04 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe().T" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot relevant feature interactions\n", + "df.kurtosis().plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Unnamed: 0 -1.200000\n", + "Madrid_wind_speed 2.036462\n", + "Bilbao_rain_1h 32.904656\n", + "Valencia_wind_speed 35.645426\n", + "Seville_humidity -1.017983\n", + "Madrid_humidity -1.167537\n", + "Bilbao_clouds_all -1.533417\n", + "Bilbao_wind_speed 3.631565\n", + "Seville_clouds_all 2.155921\n", + "Bilbao_wind_deg -1.083530\n", + "Barcelona_wind_speed 1.493635\n", + "Barcelona_wind_deg -0.959160\n", + "Madrid_clouds_all 0.142079\n", + "Seville_wind_speed 1.398580\n", + "Barcelona_rain_1h 101.578931\n", + "Seville_rain_1h 93.840746\n", + "Bilbao_snow_3h 806.128471\n", + "Barcelona_pressure 3687.564230\n", + "Seville_rain_3h 413.136592\n", + "Madrid_rain_1h 76.584491\n", + "Barcelona_rain_3h 187.800460\n", + "Valencia_snow_3h 4089.323165\n", + "Madrid_weather_id 9.259047\n", + "Barcelona_weather_id 5.701882\n", + "Bilbao_pressure 1.825323\n", + "Seville_weather_id 10.710308\n", + "Valencia_pressure 2.211823\n", + "Seville_temp_max -0.515989\n", + "Madrid_pressure 2.216199\n", + "Valencia_temp_max -0.613755\n", + "Valencia_temp -0.643793\n", + "Bilbao_weather_id 0.067814\n", + "Seville_temp -0.504132\n", + "Valencia_humidity -0.734345\n", + "Valencia_temp_min -0.599551\n", + "Barcelona_temp_max -0.728757\n", + "Madrid_temp_max -0.662861\n", + "Barcelona_temp -0.696555\n", + "Bilbao_temp_min -0.230342\n", + "Bilbao_temp -0.086363\n", + "Barcelona_temp_min -0.474890\n", + "Bilbao_temp_max 0.283366\n", + "Seville_temp_min -0.475564\n", + "Madrid_temp -0.612299\n", + "Madrid_temp_min -0.666646\n", + "load_shortfall_3h -0.118999\n", + "dtype: float64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.kurtosis()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# evaluate correlation\n", + "fig = plt.figure(figsize=(10,8));\n", + "ax = fig.add_subplot(111);\n", + "plot_corr(df.corr(), xnames = df.corr().columns, ax = ax, );" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([7.000e+01, 3.720e+02, 8.370e+02, 1.641e+03, 2.494e+03, 2.301e+03,\n", + " 9.000e+02, 1.400e+02, 7.000e+00, 1.000e+00]),\n", + " array([-6618. , -2765.8, 1086.4, 4938.6, 8790.8, 12643. , 16495.2,\n", + " 20347.4, 24199.6, 28051.8, 31904. ]),\n", + " )" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# have a look at feature distributions\n", + "plt.hist(df['load_shortfall_3h'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 4. Data Engineering\n", + "\n", + "Back to Table of Contents\n", + "\n", + "---\n", + " \n", + "| ⚡ Description: Data engineering ⚡ |\n", + "| :--------------------------- |\n", + "| In this section you are required to: clean the dataset, and possibly create new features - as identified in the EDA phase. |\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# remove missing values/ features\n", + "df['Valencia_pressure'].fillna(df['Valencia_pressure'].mean(), inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "df['Valencia_wind_deg'] = df['Valencia_wind_deg'].str.extract('(\\d+)').astype('int64')\n", + "df['Seville_pressure'] = df['Seville_pressure'].str.extract('(\\d+)').astype('int64')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# create new features\n", + "df['Year'] = df['time'].astype('datetime64').dt.year\n", + "df['Month_of_year'] = df['time'].astype('datetime64').dt.month\n", + "df['Week_of_year'] = df['time'].astype('datetime64').dt.weekofyear\n", + "df['Day_of_year'] = df['time'].astype('datetime64').dt.dayofyear\n", + "df['Day_of_month'] = df['time'].astype('datetime64').dt.day\n", + "df['Day_of_week'] = df['time'].astype('datetime64').dt.dayofweek\n", + "df['Hour_of_week'] = ((df['time'].astype('datetime64').dt.dayofweek) * 24 + 24) - (24 - df['time'].astype('datetime64').dt.hour)\n", + "df['Hour_of_day'] = df['time'].astype('datetime64').dt.hour\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Time_df = df.iloc[:,[-8,-7,-6,-5,-4,-3,-2,-1]]\n", + "plt.figure(figsize=[10,6])\n", + "sns.heatmap(Time_df.corr(),annot=True )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# engineer existing features\n", + "df = df.drop(columns=['Week_of_year','Day_of_year','Hour_of_week', 'Unnamed: 0','time'])" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "X = df.drop(columns = 'load_shortfall_3h')\n", + "y = df['load_shortfall_3h'].astype('int')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(40, 2)" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bestfeatures = SelectKBest(score_func=chi2, k=10)\n", + "fit = bestfeatures.fit(X,y)\n", + "dfscores = pd.DataFrame(fit.scores_)\n", + "dfcolumns = pd.DataFrame(X.columns)\n", + "featureScores = pd.concat([dfcolumns, dfscores], axis=1)\n", + "featureScores.columns = ['Features', 'Score']\n", + "new_X = featureScores.sort_values('Score',ascending=False).head(40)\n", + "new_X.shape " + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "X = X[['Madrid_wind_speed', 'Valencia_wind_deg', 'Bilbao_rain_1h',\n", + " 'Valencia_wind_speed', 'Seville_humidity', 'Madrid_humidity',\n", + " 'Bilbao_clouds_all', 'Bilbao_wind_speed', 'Seville_clouds_all',\n", + " 'Bilbao_wind_deg', 'Barcelona_wind_speed', 'Barcelona_wind_deg',\n", + " 'Madrid_clouds_all', 'Seville_wind_speed', 'Barcelona_rain_1h',\n", + " 'Seville_pressure', 'Seville_rain_1h', 'Bilbao_snow_3h',\n", + " 'Barcelona_pressure', 'Seville_rain_3h', 'Madrid_rain_1h',\n", + " 'Barcelona_rain_3h', 'Valencia_snow_3h', 'Madrid_weather_id',\n", + " 'Barcelona_weather_id', 'Bilbao_pressure', 'Seville_weather_id',\n", + " 'Valencia_pressure', 'Seville_temp_max', 'Bilbao_weather_id', \n", + " 'Valencia_humidity', 'Year', 'Month_of_year', 'Day_of_month', 'Day_of_week', 'Hour_of_day']]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "scaler = StandardScaler()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8763, 36)" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_scaled = scaler.fit_transform(X)\n", + "X_scaled = pd.DataFrame(X_scaled,columns=X.columns)\n", + "X_scaled.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 5. Modelling\n", + "\n", + "Back to Table of Contents\n", + "\n", + "---\n", + " \n", + "| ⚡ Description: Modelling ⚡ |\n", + "| :--------------------------- |\n", + "| In this section, you are required to create one or more regression models that are able to accurately predict the thee hour load shortfall. |\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# split data\n", + "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, train_size=0.8, test_size=0.2, random_state = 42)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training predictor: (7010, 36)\n", + "Training target: (7010,)\n", + "Testing predictor: (1753, 36)\n", + "Testing target: (1753,)\n" + ] + } + ], + "source": [ + "print('Training predictor:', X_train.shape)\n", + "print('Training target:', y_train.shape)\n", + "print('Testing predictor:', X_test.shape)\n", + "print('Testing target:', y_test.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "lm = LinearRegression()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lm.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "predict = lm.predict(X_test)\n", + "train_predict = lm.predict(X_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,\n", + " normalize=False, positive=False, precompute=False, random_state=None,\n", + " selection='cyclic', tol=0.0001, warm_start=False)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create targets and features dataset\n", + "lasso = Lasso(alpha=0.01)\n", + "lasso.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "# create one or more ML models\n", + "lasso_pred = lasso.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n", + " normalize=False, random_state=None, solver='auto', tol=0.001)" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# evaluate one or more ML models\n", + "Ridge = Ridge()\n", + "Ridge.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "Ridge_pred = Ridge.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "Sv_reg = SVR(kernel='rbf', gamma='auto') " + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',\n", + " kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Sv_reg.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "SV_pred = Sv_reg.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "Reg_tree = DecisionTreeRegressor(random_state = 42) \n", + "Reg_tree.fit(X_train,y_train)\n", + "Tree_pred = Reg_tree.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "RF = RandomForestRegressor(n_estimators=200, max_depth=8)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "RF.fit(X_train,y_train)\n", + "RF_predict = RF.predict(X_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 6. Model Performance\n", + "\n", + "Back to Table of Contents\n", + "\n", + "---\n", + " \n", + "| ⚡ Description: Model performance ⚡ |\n", + "| :--------------------------- |\n", + "| In this section you are required to compare the relative performance of the various trained ML models on a holdout dataset and comment on what model is the best and why. |\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare model performance\n", + "Linear = pd.DataFrame({'Actual': y_test, 'Predicted': predict})\n", + "Lass_ = pd.DataFrame({'Actual': y_test, 'Predicted': lasso_pred})\n", + "Ridge_ = pd.DataFrame({'Actual': y_test, 'Predicted': Ridge_pred})\n", + "Sv_ = pd.DataFrame({'Actual': y_test, 'Predicted': SV_pred})\n", + "Des_= pd.DataFrame({'Actual': y_test, 'Predicted': Tree_pred})\n", + "Rand_ = pd.DataFrame({'Actual': y_test, 'Predicted': RF_predict})" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Actual Predicted\n", + "1226 11450 12402.636182\n", + "7903 13693 12245.041069\n", + "1559 18337 11989.975721\n", + "3621 -1221 9383.384417\n", + "7552 8515 14086.322696\n" + ] + } + ], + "source": [ + "print(Linear.head()) " + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Actual Predicted\n", + "1226 11450 12402.596545\n", + "7903 13693 12245.032510\n", + "1559 18337 11989.974822\n", + "3621 -1221 9383.406078\n", + "7552 8515 14086.234318\n" + ] + } + ], + "source": [ + "print(Lass_.head()) " + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Actual Predicted\n", + "1226 11450 12402.251854\n", + "7903 13693 12244.719235\n", + "1559 18337 11990.023803\n", + "3621 -1221 9383.425692\n", + "7552 8515 14085.757950\n" + ] + } + ], + "source": [ + "print(Ridge_.head()) " + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Actual Predicted\n", + "1226 11450 11119.964057\n", + "7903 13693 11106.784848\n", + "1559 18337 11211.627698\n", + "3621 -1221 11021.139113\n", + "7552 8515 11220.062862\n" + ] + } + ], + "source": [ + "print(Sv_.head()) " + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Actual Predicted\n", + "1226 11450 8593.0\n", + "7903 13693 11016.0\n", + "1559 18337 18497.0\n", + "3621 -1221 934.0\n", + "7552 8515 10368.0\n" + ] + } + ], + "source": [ + "print(Des_.head()) " + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Actual Predicted\n", + "1226 11450 7389.806276\n", + "7903 13693 16930.667514\n", + "1559 18337 14231.584977\n", + "3621 -1221 4718.308842\n", + "7552 8515 10700.182856\n" + ] + } + ], + "source": [ + "print(Rand_.head()) " + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "Model_Performance = { \n", + " \n", + " 'Test RMSE':\n", + " \n", + " {\"Linear model\": np.sqrt(metrics.mean_squared_error(y_test,predict)),\n", + " \"Ridge\": np.sqrt(metrics.mean_squared_error(y_test,Ridge_pred)),\n", + " \"Lasso\" : np.sqrt(metrics.mean_squared_error(y_test,lasso_pred)),\n", + " \"SVR\" : np.sqrt(metrics.mean_squared_error(y_test,SV_pred)),\n", + " \"Decision Tree\" : np.sqrt(metrics.mean_squared_error(y_test,Tree_pred)),\n", + " \"Random Forest\" : np.sqrt(metrics.mean_squared_error(y_test,RF_predict))}\n", + " \n", + " }\n" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "px.bar(Model_Performance4, y =Model_Performance4['Test R^2'],\n", + " color = Model_Performance4.index, width =700, height=400)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "# Choose best model and motivate why it is the best choice (coming soon)" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "df_test['Year'] = df_test['time'].astype('datetime64').dt.year\n", + "df_test['Month_of_year'] = df_test['time'].astype('datetime64').dt.month\n", + "df_test['Week_of_year'] = df_test['time'].astype('datetime64').dt.weekofyear\n", + "df_test['Day_of_year'] = df_test['time'].astype('datetime64').dt.dayofyear\n", + "df_test['Day_of_month'] = df_test['time'].astype('datetime64').dt.day\n", + "df_test['Day_of_week'] = df_test['time'].astype('datetime64').dt.dayofweek\n", + "df_test['Hour_of_week'] = ((df_test['time'].astype('datetime64').dt.dayofweek) * 24 + 24) - (24 - df_test['time'].astype('datetime64').dt.hour)\n", + "df_test['Hour_of_day'] = df_test['time'].astype('datetime64').dt.hour\n" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "time = df_test['time']" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "df_test = df_test[['Madrid_wind_speed', 'Valencia_wind_deg', 'Bilbao_rain_1h',\n", + " 'Valencia_wind_speed', 'Seville_humidity', 'Madrid_humidity',\n", + " 'Bilbao_clouds_all', 'Bilbao_wind_speed', 'Seville_clouds_all',\n", + " 'Bilbao_wind_deg', 'Barcelona_wind_speed', 'Barcelona_wind_deg',\n", + " 'Madrid_clouds_all', 'Seville_wind_speed', 'Barcelona_rain_1h',\n", + " 'Seville_pressure', 'Seville_rain_1h', 'Bilbao_snow_3h',\n", + " 'Barcelona_pressure', 'Seville_rain_3h', 'Madrid_rain_1h',\n", + " 'Barcelona_rain_3h', 'Valencia_snow_3h', 'Madrid_weather_id',\n", + " 'Barcelona_weather_id', 'Bilbao_pressure', 'Seville_weather_id',\n", + " 'Valencia_pressure', 'Seville_temp_max', 'Bilbao_weather_id', \n", + " 'Valencia_humidity', 'Year', 'Month_of_year', 'Day_of_month', 'Day_of_week', 'Hour_of_day']]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "df_test['Valencia_wind_deg'] = df_test['Valencia_wind_deg'].str.extract('(\\d+)').astype('int64')\n", + "df_test['Seville_pressure'] = df_test['Seville_pressure'].str.extract('(\\d+)').astype('int64')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "df_test['load_shortfall_3h'] = RF.predict(df_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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timeload_shortfall_3h
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" + ], + "text/plain": [ + " time load_shortfall_3h\n", + "0 2018-01-01 00:00:00 8368.756771\n", + "1 2018-01-01 03:00:00 10549.049977\n", + "2 2018-01-01 06:00:00 11137.764140\n", + "3 2018-01-01 09:00:00 10766.926316\n", + "4 2018-01-01 12:00:00 10887.099763\n", + "... ... ...\n", + "2915 2018-12-31 09:00:00 12334.246361\n", + "2916 2018-12-31 12:00:00 12583.993891\n", + "2917 2018-12-31 15:00:00 12008.711919\n", + "2918 2018-12-31 18:00:00 11988.023776\n", + "2919 2018-12-31 21:00:00 11577.758250\n", + "\n", + "[2920 rows x 2 columns]" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_test['time'] = time\n", + "load = df_test[['time','load_shortfall_3h']]\n", + "load.to_csv('submission_predict.csv', index = False)\n", + "load\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 7. Model Explanations\n", + "\n", + "Back to Table of Contents\n", + "\n", + "---\n", + " \n", + "| ⚡ Description: Model explanation ⚡ |\n", + "| :--------------------------- |\n", + "| In this section, you are required to discuss how the best performing model works in a simple way so that both technical and non-technical stakeholders can grasp the intuition behind the model's inner workings. |\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "# discuss chosen methods logic (coming soon! in progress)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}