Chipotle_clustering_challenge-Califonia.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chipotle clustering challenge\n",
    "\n",
    "- Repository: `clustering`\n",
    "- Type of Challenge: `Learning`\n",
    "- Duration: `1 day`\n",
    "- Deployment strategy :\n",
    "\t- Github page\n",
    "- Team challenge : `yes (max 4)`\n",
    "\t- visualization: 1\n",
    "\t- data analysis: 2\n",
    "\t- project management : 1\n",
    "\n",
    "## Learning Objectives \n",
    "\n",
    "- visualization: to be able to use geopandas, matplotlib (and seaborn) to visualize clustered data onto a map\n",
    "- data analysis: to be able to determine appropriate clustering methods and variables to cluster \n",
    "- data analysis: to evaluate the chosen clustering method in comparison to other methods \n",
    "- project managment: to be able to allot the right amount of time for the tasks and present the results onto a readable github page\n",
    "\n",
    "## The Mission\n",
    "\n",
    "<img src=\"https://media.giphy.com/media/l3vR4Fp4U1DhW8bhS/giphy.gif\" align=\"right\" width=\"450\"/>\n",
    "\n",
    ">\"Honey, where do you wanna eat?\"\n",
    ">\n",
    ">\"Do you even have to ask? Chipotle, of course!\"\n",
    ">\n",
    ">\"How could I forget, we've been eating chipotle for the last 47 days...\"\n",
    ">\n",
    ">\"So get ready for number 48! Oh I can't wait to stuff my face with a burrito!\"\n",
    ">\n",
    ">\"But the closest one is an hour drive away, couldn't we just get mcdonalds?\"\n",
    ">\n",
    ">\"How dare you utter that word in my presence? First the neighbours, now you!\"\n",
    ">\n",
    ">\"The neighbours? What do they have to d-\"\n",
    ">\n",
    ">\"That's it, we're moving, I can't deal with this anti-chipotle fascism.\"\n",
    "\n",
    "Find chipotle **epicentres** to live your ideal chipotle lifestyle by clustering the [chipotle](./assets/chipotle_locations.csv) dataset.\n",
    "\n",
    "### Must-have features\n",
    "\n",
    "- A visualisation of the USA with chipotle locations\n",
    "- Visualization of the different clusters\n",
    "- Intrinsic analysis comparison of the clusters of at least 2 methods with varying arguments (using euclidian distance as criteria)\n",
    "- A chosen centroid to live. Make your argument of why the chosen centroid is superior to others. Examples of arguments are:\n",
    "    - highest density\n",
    "    - greatest uninterrupted link of chipotle locations with smallest link-to-link distance\n",
    "    - ...\n",
    "- a Github page where results are visualized\n",
    "\n",
    "\n",
    "### Nice-to-have features\n",
    "\n",
    "- Colour coded cluster visualisation\n",
    "- Clear graph legends\n",
    "\n",
    "## Deliverables\n",
    "1. Publish your source code on the GitHub repository.\n",
    "2. Make a clear github page explaining your results and methods\n",
    "3. A chosen adress for the couple to live\n",
    "\n",
    "### Steps\n",
    "1. Create the repository\n",
    "2. Install geopandas\n",
    "3. Plot the [US map](https://jcutrer.com/python/learn-geopandas-plotting-usmaps)\n",
    "4. Visualize your data on this map\n",
    "5. Plot a [dendogram](https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_dendrogram.html) of your data to help you decide the appropropriate clustering resolution\n",
    "6. Compare and analyse different [clustering methods](https://scikit-learn.org/stable/modules/clustering.html) using intrinsic analysis to decide on a chosen method.\n",
    "7. Choose a centroid/adress to live\n",
    "8. Publish your results to a Github page with an explanation of your method.\n",
    "\n",
    "## Evaluation criterias\n",
    "| Criteria       | Indicator                                                                             | Yes/No |\n",
    "|----------------|---------------------------------------------------------------------------------------|--------|\n",
    "| 1. Is complete | The student has realized all must-have features.                                      |        |\n",
    "|                | There is a published GitHub page available showing the visualized results                                           |        | A US location has been selected an arguments for choosing it has been given\n",
    "|                | ...                               \t\t\t\t\t                                 |        |\n",
    "|                | ...                                                                                   |        |\n",
    "| 3. Is Correct  | All participants have contributed to the git repository\t\t\t\t\t\t\t\t\t\t                                         \t |        |\n",
    "|                | All data is visualised clearly and interpretably\t\t\t\t\t\t\t\t                                                     |        | The method and progress of the project is detailed in the git repository\n",
    "\n",
    "\n",
    "## A final note of encouragement\n",
    "\n",
    "\n",
    "![You've got this!](https://media.giphy.com/media/gJuZSbDxv0zLTMdafV/giphy.gif)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./assets/chipotle_stores.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>location</th>\n",
       "      <th>address</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Auburn</td>\n",
       "      <td>346 W Magnolia Ave Auburn, AL 36832 US</td>\n",
       "      <td>32.606813</td>\n",
       "      <td>-85.487328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>300 20th St S Birmingham, AL 35233 US</td>\n",
       "      <td>33.509721</td>\n",
       "      <td>-86.802756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>3220 Morrow Rd Birmingham, AL 35235 US</td>\n",
       "      <td>33.595581</td>\n",
       "      <td>-86.647437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>4719 Highway 280 Birmingham, AL 35242 US</td>\n",
       "      <td>33.422582</td>\n",
       "      <td>-86.698279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Cullman</td>\n",
       "      <td>1821 Cherokee Ave SW Cullman, AL 35055 US</td>\n",
       "      <td>34.154134</td>\n",
       "      <td>-86.841220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     state    location                                    address   latitude  \\\n",
       "0  Alabama      Auburn     346 W Magnolia Ave Auburn, AL 36832 US  32.606813   \n",
       "1  Alabama  Birmingham      300 20th St S Birmingham, AL 35233 US  33.509721   \n",
       "2  Alabama  Birmingham     3220 Morrow Rd Birmingham, AL 35235 US  33.595581   \n",
       "3  Alabama  Birmingham   4719 Highway 280 Birmingham, AL 35242 US  33.422582   \n",
       "4  Alabama     Cullman  1821 Cherokee Ave SW Cullman, AL 35055 US  34.154134   \n",
       "\n",
       "   longitude  \n",
       "0 -85.487328  \n",
       "1 -86.802756  \n",
       "2 -86.647437  \n",
       "3 -86.698279  \n",
       "4 -86.841220  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Alabama', 'Arizona', 'Arkansas', 'California', 'Colorado',\n",
       "       'Connecticut', 'Delaware', 'Florida', 'Georgia', 'Idaho',\n",
       "       'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana',\n",
       "       'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota',\n",
       "       'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada',\n",
       "       'New Hampshire', 'New Jersey', 'New Mexico', 'New York',\n",
       "       'North Carolina', 'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania',\n",
       "       'Rhode Island', 'South Carolina', 'Tennessee', 'Texas', 'Utah',\n",
       "       'Vermont', 'Virginia', 'Washington', 'Washington DC',\n",
       "       'West Virginia', 'Wisconsin', 'North Dakota', 'Wyoming'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.state.unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California        421\n",
       "Texas             226\n",
       "Ohio              193\n",
       "Florida           177\n",
       "New York          160\n",
       "Illinois          144\n",
       "Virginia          107\n",
       "Pennsylvania       96\n",
       "Maryland           94\n",
       "Arizona            85\n",
       "Colorado           79\n",
       "Minnesota          71\n",
       "New Jersey         69\n",
       "North Carolina     65\n",
       "Massachusetts      62\n",
       "Georgia            61\n",
       "Washington         43\n",
       "Indiana            40\n",
       "Michigan           39\n",
       "Missouri           39\n",
       "Oregon             32\n",
       "Kansas             30\n",
       "Nevada             29\n",
       "Tennessee          26\n",
       "Connecticut        24\n",
       "Washington DC      21\n",
       "South Carolina     21\n",
       "Kentucky           21\n",
       "Wisconsin          20\n",
       "Alabama            15\n",
       "Oklahoma           12\n",
       "Utah               11\n",
       "Nebraska           10\n",
       "Iowa               10\n",
       "Louisiana          10\n",
       "Rhode Island        9\n",
       "New Mexico          9\n",
       "Delaware            9\n",
       "New Hampshire       8\n",
       "Arkansas            6\n",
       "West Virginia       6\n",
       "Maine               5\n",
       "Idaho               4\n",
       "Montana             3\n",
       "North Dakota        2\n",
       "Vermont             2\n",
       "Mississippi         2\n",
       "Wyoming             1\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['state'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chose to live at Califonia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from scipy.spatial.distance import cdist\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import silhouette_score\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "filt = df.state =='California'\n",
    "\n",
    "df_ca = df[filt] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ca.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ca = df_ca[['latitude', 'longitude']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.756121</td>\n",
       "      <td>-122.250114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.786141</td>\n",
       "      <td>-122.281207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34.092555</td>\n",
       "      <td>-118.145842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.576980</td>\n",
       "      <td>-117.725426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.839580</td>\n",
       "      <td>-117.888675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>33.941058</td>\n",
       "      <td>-118.046211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>33.944400</td>\n",
       "      <td>-117.994826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>34.169821</td>\n",
       "      <td>-118.605285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>34.187712</td>\n",
       "      <td>-118.598003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>39.142583</td>\n",
       "      <td>-121.629762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>421 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      latitude   longitude\n",
       "0    37.756121 -122.250114\n",
       "1    37.786141 -122.281207\n",
       "2    34.092555 -118.145842\n",
       "3    33.576980 -117.725426\n",
       "4    33.839580 -117.888675\n",
       "..         ...         ...\n",
       "416  33.941058 -118.046211\n",
       "417  33.944400 -117.994826\n",
       "418  34.169821 -118.605285\n",
       "419  34.187712 -118.598003\n",
       "420  39.142583 -121.629762\n",
       "\n",
       "[421 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters = range(1,30)\n",
    "\n",
    "meanDistortions = []\n",
    "\n",
    "for k in clusters:\n",
    "    km = KMeans(n_clusters=k)\n",
    "    km.fit(df_ca)\n",
    "    predict=km.predict(df_ca)\n",
    "    meanDistortions.append(sum(np.min(cdist(df_ca,km.cluster_centers_, 'euclidean'), axis=1)) / \n",
    "                           df_ca.shape[0])\n",
    "\n",
    "plt.plot(clusters, meanDistortions, 'ro-')\n",
    "\n",
    "plt.title('Selecting k with the Elbow Method')\n",
    "\n",
    "plt.xlabel('k');\n",
    "\n",
    "plt.ylabel('Average distortion');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit a kmeans object to the dataset\n",
    "kmeans = KMeans(n_clusters=5).fit(df_ca)  \n",
    "\n",
    "# clusters is an attribute of the object\n",
    "cluster_centers = kmeans.cluster_centers_\n",
    "\n",
    "# add cluster index to dataframe\n",
    "cluster_labels = pd.Series(kmeans.labels_, name='cluster')\n",
    "df_ca = df_ca.join(cluster_labels.to_frame())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat = []\n",
    "lon = []\n",
    "for i in cluster_centers:\n",
    "    lon.append(i[0])\n",
    "    lat.append(i[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cluster = pd.DataFrame(lat,lon).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cluster.columns = ['lat', 'lon']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "<b>%{hovertext}</b><br><br>latitude=%{lat}<br>longitude=%{lon}<br>cluster=%{marker.color}<extra></extra>",
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   "source": [
    "fig_px = px.scatter_mapbox(df_ca, lat=\"latitude\", lon=\"longitude\",color=\"cluster\",hover_name=\"cluster\", zoom=5, height=500)\n",
    "\n",
    "fig_px.update_layout(mapbox_style=\"open-street-map\",margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
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   "metadata": {},
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