lasso.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b622f76b-67f8-4032-9b68-66f6d6080fdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import Lasso\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9a85929a-df18-474a-9e90-ed900c956f82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.049750717221652696"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./uscities.csv\")\n",
    "X = df[[\"lat\", \"lng\", \"population\"]]\n",
    "y = df.density\n",
    "label = df.city\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "train_label, test_label = label[X_train.index], label[X_test.index]\n",
    "\n",
    "model = Lasso()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8906d1ce-d096-468f-bb69-0b1ba090659e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([485.0311743 , 499.77514462, 488.21123777, ..., 289.64567217,\n",
       "       456.10231027, 490.61208806])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "891fae5a-d1df-432f-866f-b967add06607",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='y_True', ylabel='y_pred'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = model.predict(X_test)\n",
    "\n",
    "results = pd.DataFrame({\"y_True\":np.array(y_test), \"y_pred\":model.predict(X_test)}, index=test_label)\n",
    "\n",
    "results.plot(kind=\"scatter\", x=\"y_True\", y=\"y_pred\")"
   ]
  }
 ],
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