train_test_split.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
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   "source": [
    "X=np.random.normal(0,1,100)\n",
    "Y=np.random.uniform(0,1,100)\n",
    "\n",
    "X.shape, Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "          X         Y\n",
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   "source": [
    "df=pd.DataFrame(np.vstack((X,Y)).transpose(),columns=['X','Y'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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     "execution_count": 4,
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   "source": [
    "np.random.seed(0)\n",
    "\n",
    "TRAIN_PCT=0.8\n",
    "VAL_PCT=1-TRAIN_PCT\n",
    "\n",
    "TRAIN_SIZE=int(len(df)*TRAIN_PCT)\n",
    "VAL_SIZE=len(df)-TRAIN_SIZE\n",
    "\n",
    "idx=np.arange(len(df))\n",
    "np.random.shuffle(idx)\n",
    "\n",
    "train_idx=idx[:TRAIN_SIZE]\n",
    "val_idx=idx[TRAIN_SIZE:]\n",
    "\n",
    "train_df=df.loc[train_idx]\n",
    "val_df=df.loc[val_idx]\n",
    "\n",
    "X_train=train_df[['X']]\n",
    "y_train=train_df['Y']\n",
    "\n",
    "X_val=val_df[['X']]\n",
    "y_val=val_df['Y']\n",
    "\n",
    "rgn=LinearRegression()\n",
    "rgn.fit(X_train,y_train)\n",
    "\n",
    "y_pred=rgn.predict(X_val)\n",
    "r2_score(y_val, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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     "execution_count": 8,
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   "source": [
    "X=df[['X']]\n",
    "y=df['Y']\n",
    "\n",
    "X_train, X_test, y_train, y_test=train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "rgn.fit(X_train,y_train)\n",
    "y_pred=rgn.predict(X_test)\n",
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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