Added Association Rule Leaning Models and dataset. Updated the README

Association Rule Learning/Apriori/apriori.ipynb

@@ -0,0 +1,169 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "1fziHl7Ar94J"
+   },
+   "source": [
+    "# Apriori"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "eiNwni1xsEgT"
+   },
+   "source": [
+    "## Importing the libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "UJfitBClsJlT"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "vLt-7XUKsXBd"
+   },
+   "source": [
+    "## Data Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "J_A-UFOAsaDf"
+   },
+   "outputs": [],
+   "source": [
+    "dataset = pd.read_csv('../../datasets/Market_Basket_Optimisation.csv', header = None)\n",
+    "transactions = []\n",
+    "for i in range(0, 7501):\n",
+    "    transactions.append([str(dataset.values[i,j]) for j in range(0, 20)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "1wYZdBd5sea_"
+   },
+   "source": [
+    "## Training the Apriori model on the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "YzIk4vXZsj5i"
+   },
+   "outputs": [],
+   "source": [
+    "# WARNING: Make sure to upload the apyori.py file into this Colab notebook before running this cell\n",
+    "from apyori import apriori\n",
+    "rules = apriori(transactions, min_support = 0.003, min_confidence = 0.2, min_lift = 3, min_length = 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "b176YNwWspiO"
+   },
+   "source": [
+    "## Visualising the results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "a4ZyVYazsvWt"
+   },
+   "outputs": [],
+   "source": [
+    "results = list(rules)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 54
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 941,
+     "status": "ok",
+     "timestamp": 1586415085286,
+     "user": {
+      "displayName": "Hadelin de Ponteves",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEuXdT7eQweUmRPW8_laJuPggSK6hfvpl5a6WBaA=s64",
+      "userId": "15047218817161520419"
+     },
+     "user_tz": -240
+    },
+    "id": "FdaOb_95tQoP",
+    "outputId": "701cb6ce-3951-4c6e-9140-c867741bd885"
+   },
+   "outputs": [],
+   "source": [
+    "print(results)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "authorship_tag": "ABX9TyO41Q6DRmOTeVEfityPN5hB",
+   "collapsed_sections": [],
+   "name": "Apriori",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "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"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

README.md

@@ -13,17 +13,19 @@ The repository is composed of the following sections:
   * Support Vector Regression
   * Decision Tree Regression
   * Random Forest Regression
-* `Classification` : this folder contains Jupyter notebooks for the following Classification Models:
+* `Classification` : This folder contains Jupyter notebooks for the following Classification Models:
   * Logistic Regression
   * K - Nearest Neighbor (K-NN)
   * Support Vector Machine (SVM)
   * Kernel SVM
   * Naive Bayes
   * Decision Tree Classification
   * Random Forest Classification
-* `Clustering` : This folder contain Jupyter notebooks for the following Clustering Models:
+* `Clustering` : This folder contains Jupyter notebooks for the following Clustering Models:
   * K-Means Clustering
   * Hierarchical Clustering
+* `Association Rule Learning` : This folder contains Jupyter notebooks for the following Association Rule Learning Models:
+  * Apriori