create_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random as rnd\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulate GEO data from colombia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "amount_sections=2\n",
    "sections=range(amount_sections)\n",
    "\n",
    "limit_upper_lat=[4.755422,4.678025]\n",
    "limit_lower_lat=[4.663925,4.553890]\n",
    "limit_upper_lon=[74.075022,74.135649]\n",
    "limit_lower_lon=[74.047307,74.087928]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\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>id</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>delivery_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4.722030</td>\n",
       "      <td>-74.074603</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4.739089</td>\n",
       "      <td>-74.059220</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4.747405</td>\n",
       "      <td>-74.074499</td>\n",
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       "    <tr>\n",
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       "      <td>4.748768</td>\n",
       "      <td>-74.066084</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>45</td>\n",
       "      <td>4.582651</td>\n",
       "      <td>-74.104416</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>46</td>\n",
       "      <td>4.562518</td>\n",
       "      <td>-74.132985</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>47</td>\n",
       "      <td>4.574412</td>\n",
       "      <td>-74.123077</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>48</td>\n",
       "      <td>4.670580</td>\n",
       "      <td>-74.098817</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>49</td>\n",
       "      <td>4.663153</td>\n",
       "      <td>-74.127254</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id       lat        lon  delivery_day\n",
       "0    0  4.722030 -74.074603             3\n",
       "1    1  4.739089 -74.059220             6\n",
       "2    2  4.747405 -74.074499             5\n",
       "3    3  4.705417 -74.055711             3\n",
       "4    4  4.748768 -74.066084             3\n",
       "..  ..       ...        ...           ...\n",
       "95  45  4.582651 -74.104416             5\n",
       "96  46  4.562518 -74.132985             4\n",
       "97  47  4.574412 -74.123077             6\n",
       "98  48  4.670580 -74.098817             5\n",
       "99  49  4.663153 -74.127254             4\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lat_list=[]\n",
    "lon_list=[]\n",
    "id_data=[]\n",
    "day=[]\n",
    "\n",
    "for j in sections:\n",
    "    for i in range (50):\n",
    "        lat =rnd.uniform(limit_lower_lat[j],limit_upper_lat[j])\n",
    "        lon=-rnd.uniform(limit_lower_lon[j],limit_upper_lon[j])\n",
    "        lat_list.append(lat)\n",
    "        lon_list.append(lon)\n",
    "        id_data.append(i)\n",
    "        day.append(rnd.randint(1,6))\n",
    "\n",
    "d = {'id':id_data,'lat': lat_list, 'lon': lon_list, 'delivery_day' : day}\n",
    "df = pd.DataFrame(data=d)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "\n",
    "fig = px.scatter_mapbox(df , lat=\"lat\" , lon=\"lon\" , hover_name=\"id\" , hover_data=[\"id\"] ,color= \"delivery_day\" , zoom = 10 , height = 500,color_continuous_scale=px.colors.cyclical.IceFire)\n",
    "fig.update_layout(mapbox_style= \"carto-positron\")\n",
    "fig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
    "fig.show()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Kmeans cluster algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning:\n",
      "\n",
      "KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(n_clusters=6, random_state=0, n_init=\"auto\",).fit(df[['lat','lon']])\n",
    "\n",
    "df['cluster']=kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
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           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
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          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
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           "yaxis": {
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            "gridwidth": 2,
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            "showbackground": true,
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           "zaxis": {
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            "gridwidth": 2,
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            "showbackground": true,
            "ticks": "",
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            "linecolor": "white",
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           "bgcolor": "#E5ECF6",
           "caxis": {
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            "linecolor": "white",
            "ticks": ""
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          "title": {
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          },
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           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
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           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
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           "zerolinewidth": 2
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       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.scatter_mapbox(df , lat=\"lat\" , lon=\"lon\" , hover_name=\"id\" , hover_data=[\"id\"] ,color= \"cluster\" , zoom = 10 , height = 500,color_continuous_scale=px.colors.cyclical.IceFire)\n",
    "fig.update_layout(mapbox_style=\"carto-positron\")\n",
    "fig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>id_data</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4.738238</td>\n",
       "      <td>-74.058181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4.716736</td>\n",
       "      <td>-74.055550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4.698153</td>\n",
       "      <td>-74.062498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4.725417</td>\n",
       "      <td>-74.073281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4.676325</td>\n",
       "      <td>-74.048914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>995</td>\n",
       "      <td>4.619128</td>\n",
       "      <td>-74.135562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>996</td>\n",
       "      <td>4.596469</td>\n",
       "      <td>-74.104350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>997</td>\n",
       "      <td>4.556092</td>\n",
       "      <td>-74.108706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>998</td>\n",
       "      <td>4.654948</td>\n",
       "      <td>-74.121960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>999</td>\n",
       "      <td>4.654045</td>\n",
       "      <td>-74.095522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id_data       lat        lon\n",
       "0           0  4.738238 -74.058181\n",
       "1           1  4.716736 -74.055550\n",
       "2           2  4.698153 -74.062498\n",
       "3           3  4.725417 -74.073281\n",
       "4           4  4.676325 -74.048914\n",
       "...       ...       ...        ...\n",
       "1995      995  4.619128 -74.135562\n",
       "1996      996  4.596469 -74.104350\n",
       "1997      997  4.556092 -74.108706\n",
       "1998      998  4.654948 -74.121960\n",
       "1999      999  4.654045 -74.095522\n",
       "\n",
       "[2000 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lat_list=[]\n",
    "lon_list=[]\n",
    "id_data=[]\n",
    "\n",
    "for j in sections:\n",
    "    for i in range (1000):\n",
    "        lat =rnd.uniform(limit_lower_lat[j],limit_upper_lat[j])\n",
    "        lon=-rnd.uniform(limit_lower_lon[j],limit_upper_lon[j])\n",
    "        lat_list.append(lat)\n",
    "        lon_list.append(lon)\n",
    "        id_data.append(i)\n",
    "        day.append(rnd.randint(1,6))\n",
    "\n",
    "d = {'id_data':id_data,'lat': lat_list, 'lon': lon_list}\n",
    "df_new = pd.DataFrame(data=d)\n",
    "\n",
    "df_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict NEW DATA "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Kmeans to predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "df_new[\"new_cluster\"]=kmeans.predict(df_new[['lat','lon']])\n",
    "\n",
    "fig = px.scatter_mapbox(df_new , lat=\"lat\" , lon=\"lon\" , hover_name=\"id_data\" , hover_data=[\"id_data\"] ,color= \"new_cluster\" , zoom = 10 , height = 500,color_continuous_scale=px.colors.cyclical.IceFire)\n",
    "fig.update_layout(mapbox_style=\"carto-positron\")\n",
    "fig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using KNN to predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of K-NN classifier on training set: 0.93\n",
      "Accuracy of K-NN classifier on test set: 0.96\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "n_neighbors = 5\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df[['lat','lon']], df[\"cluster\"], random_state=0) \n",
    "knn = KNeighborsClassifier(n_neighbors)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "print('Accuracy of K-NN classifier on training set: {:.2f}'\n",
    "     .format(knn.score(X_train, y_train)))\n",
    "print('Accuracy of K-NN classifier on test set: {:.2f}'\n",
    "     .format(knn.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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          "scene": {
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           "bgcolor": "#E5ECF6",
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           "x": 0.05
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          "xaxis": {
           "automargin": true,
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           "linecolor": "white",
           "ticks": "",
           "title": {
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           "zerolinecolor": "white",
           "zerolinewidth": 2
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          "yaxis": {
           "automargin": true,
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     },
     "metadata": {},
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   ],
   "source": [
    "df_new[\"new_cluster\"]=knn.predict(df_new[[\"lat\",\"lon\"]])\n",
    "fig = px.scatter_mapbox(df_new , lat=\"lat\" , lon=\"lon\" , hover_name=\"id_data\" , hover_data=[\"id_data\"] ,color= \"new_cluster\" , zoom = 10 , height = 500,color_continuous_scale=px.colors.cyclical.IceFire)\n",
    "fig.update_layout(mapbox_style=\"carto-positron\")\n",
    "fig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save model (export)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# save the model to disk\n",
    "filename = 'finalized_model_KNN.sav'\n",
    "pickle.dump(knn, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "https://www.python.org/ftp/python/3.11.8/Python-3.11.8.tgz"
   ]
  }
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