Task for MLTS Exercise 8

08_09_RNN_LSTM/MLTS_Exercise_08_Data_Exploration.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>MLTS Exercise 08 - Data Exploration</h1>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Your task is to read, explore and preprocess the following timeseries dataset. All information you will gather will be useful for the next notebook, were we will train a model based on this data.\n",
+    "\n",
+    "The dataset can be downloaded from [Individual Household Electric Power Consumption](https://archive.ics.uci.edu/dataset/235/individual+household+electric+power+consumption)\n",
+    "\n",
+    "It contains \"Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Reference**  \n",
+    "Hebrail, G. & Berard, A. (2006). Individual Household Electric Power Consumption [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C58K54."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import packages\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing dataset\n",
+    "path = 'data/household_power_consumption.txt'\n",
+    "\n",
+    "Household_consumption = pd.read_csv(path, sep=';', low_memory=False)\n",
+    "Household_consumption"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocess Data\n",
+    "\n",
+    "* Convert seperate date and time columns into datetime column\n",
+    "* Convert numeric columns to correct type\n",
+    "* Find and replace missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore the data\n",
+    "\n",
+    "Find different trends by looking at:\n",
+    "* Monthly Global Active Power\n",
+    "* Energy Usage Comparison Across Sub-Meterings\n",
+    "* Proportion of Total Energy Usage by Sub-Metering\n",
+    "* Total Global Active Power Consumption by Month\n",
+    "* Total Global Active Power Consumption by Day of the Week\n",
+    "* Total Global Active Power Consumption by Hour of the Day\n",
+    "* Average Hourly Global Active Power Consumption\n",
+    "\n",
+    "Its recommended to use seaborn for some of these plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Time Series Specific Data Analysis\n",
+    "\n",
+    "Look at the\n",
+    "* Daily Global Active Power Consumption with 7-Day Rolling Average\n",
+    "* Daily Global Active Power Consumption with 30-Day Rolling Average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

08_09_RNN_LSTM/requirements.txt

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+matplotlib==3.9.2
+numpy==2.1.2
+ipykernel==6.29.5
+ipympl==0.9.4
+pandas==2.2.3
+seaborn==0.13.2
+torch==2.5.1