Simple_Gem_Price_Prediction .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f9124756",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "16a26695",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "57da0c1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../DATA/fake_reg.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "826cfdec",
   "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>price</th>\n",
       "      <th>feature1</th>\n",
       "      <th>feature2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>461.527929</td>\n",
       "      <td>999.787558</td>\n",
       "      <td>999.766096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>548.130011</td>\n",
       "      <td>998.861615</td>\n",
       "      <td>1001.042403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>410.297162</td>\n",
       "      <td>1000.070267</td>\n",
       "      <td>998.844015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>540.382220</td>\n",
       "      <td>999.952251</td>\n",
       "      <td>1000.440940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>546.024553</td>\n",
       "      <td>1000.446011</td>\n",
       "      <td>1000.338531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>476.526078</td>\n",
       "      <td>1000.018988</td>\n",
       "      <td>999.672732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>457.313186</td>\n",
       "      <td>998.855379</td>\n",
       "      <td>1000.020026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>456.720992</td>\n",
       "      <td>1001.451646</td>\n",
       "      <td>998.847606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>403.315576</td>\n",
       "      <td>1000.771023</td>\n",
       "      <td>998.562851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>599.367093</td>\n",
       "      <td>999.232244</td>\n",
       "      <td>1001.451407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          price     feature1     feature2\n",
       "0    461.527929   999.787558   999.766096\n",
       "1    548.130011   998.861615  1001.042403\n",
       "2    410.297162  1000.070267   998.844015\n",
       "3    540.382220   999.952251  1000.440940\n",
       "4    546.024553  1000.446011  1000.338531\n",
       "..          ...          ...          ...\n",
       "995  476.526078  1000.018988   999.672732\n",
       "996  457.313186   998.855379  1000.020026\n",
       "997  456.720992  1001.451646   998.847606\n",
       "998  403.315576  1000.771023   998.562851\n",
       "999  599.367093   999.232244  1001.451407\n",
       "\n",
       "[1000 rows x 3 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4ca51fb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x13c7bd070>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "45f955fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e6b4195a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mDEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "Requirement already satisfied: sklearn in /usr/local/lib/python3.9/site-packages (0.0)\n",
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.9/site-packages (from sklearn) (1.0.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.9/site-packages (from scikit-learn->sklearn) (3.1.0)\n",
      "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.9/site-packages (from scikit-learn->sklearn) (1.8.0)\n",
      "Requirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.9/site-packages (from scikit-learn->sklearn) (1.22.2)\n",
      "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.9/site-packages (from scikit-learn->sklearn) (1.1.0)\n",
      "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.0.3 is available.\n",
      "You should consider upgrading via the '/usr/local/opt/python@3.9/bin/python3.9 -m pip install --upgrade pip' command.\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2f553b31",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['feature1','feature2']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "51a787d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 999.78755752,  999.7660962 ],\n",
       "       [ 998.86161491, 1001.04240315],\n",
       "       [1000.07026691,  998.84401463],\n",
       "       ...,\n",
       "       [1001.45164617,  998.84760554],\n",
       "       [1000.77102275,  998.56285086],\n",
       "       [ 999.2322436 , 1001.45140713]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bd236456",
   "metadata": {},
   "outputs": [],
   "source": [
    "y= df['price'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7eb0bdb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d5bfa2a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(700, 2)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3f3c89aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c50f94d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class MinMaxScaler in module sklearn.preprocessing._data:\n",
      "\n",
      "class MinMaxScaler(sklearn.base._OneToOneFeatureMixin, sklearn.base.TransformerMixin, sklearn.base.BaseEstimator)\n",
      " |  MinMaxScaler(feature_range=(0, 1), *, copy=True, clip=False)\n",
      " |  \n",
      " |  Transform features by scaling each feature to a given range.\n",
      " |  \n",
      " |  This estimator scales and translates each feature individually such\n",
      " |  that it is in the given range on the training set, e.g. between\n",
      " |  zero and one.\n",
      " |  \n",
      " |  The transformation is given by::\n",
      " |  \n",
      " |      X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))\n",
      " |      X_scaled = X_std * (max - min) + min\n",
      " |  \n",
      " |  where min, max = feature_range.\n",
      " |  \n",
      " |  This transformation is often used as an alternative to zero mean,\n",
      " |  unit variance scaling.\n",
      " |  \n",
      " |  Read more in the :ref:`User Guide <preprocessing_scaler>`.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  feature_range : tuple (min, max), default=(0, 1)\n",
      " |      Desired range of transformed data.\n",
      " |  \n",
      " |  copy : bool, default=True\n",
      " |      Set to False to perform inplace row normalization and avoid a\n",
      " |      copy (if the input is already a numpy array).\n",
      " |  \n",
      " |  clip : bool, default=False\n",
      " |      Set to True to clip transformed values of held-out data to\n",
      " |      provided `feature range`.\n",
      " |  \n",
      " |      .. versionadded:: 0.24\n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  min_ : ndarray of shape (n_features,)\n",
      " |      Per feature adjustment for minimum. Equivalent to\n",
      " |      ``min - X.min(axis=0) * self.scale_``\n",
      " |  \n",
      " |  scale_ : ndarray of shape (n_features,)\n",
      " |      Per feature relative scaling of the data. Equivalent to\n",
      " |      ``(max - min) / (X.max(axis=0) - X.min(axis=0))``\n",
      " |  \n",
      " |      .. versionadded:: 0.17\n",
      " |         *scale_* attribute.\n",
      " |  \n",
      " |  data_min_ : ndarray of shape (n_features,)\n",
      " |      Per feature minimum seen in the data\n",
      " |  \n",
      " |      .. versionadded:: 0.17\n",
      " |         *data_min_*\n",
      " |  \n",
      " |  data_max_ : ndarray of shape (n_features,)\n",
      " |      Per feature maximum seen in the data\n",
      " |  \n",
      " |      .. versionadded:: 0.17\n",
      " |         *data_max_*\n",
      " |  \n",
      " |  data_range_ : ndarray of shape (n_features,)\n",
      " |      Per feature range ``(data_max_ - data_min_)`` seen in the data\n",
      " |  \n",
      " |      .. versionadded:: 0.17\n",
      " |         *data_range_*\n",
      " |  \n",
      " |  n_features_in_ : int\n",
      " |      Number of features seen during :term:`fit`.\n",
      " |  \n",
      " |      .. versionadded:: 0.24\n",
      " |  \n",
      " |  n_samples_seen_ : int\n",
      " |      The number of samples processed by the estimator.\n",
      " |      It will be reset on new calls to fit, but increments across\n",
      " |      ``partial_fit`` calls.\n",
      " |  \n",
      " |  feature_names_in_ : ndarray of shape (`n_features_in_`,)\n",
      " |      Names of features seen during :term:`fit`. Defined only when `X`\n",
      " |      has feature names that are all strings.\n",
      " |  \n",
      " |      .. versionadded:: 1.0\n",
      " |  \n",
      " |  See Also\n",
      " |  --------\n",
      " |  minmax_scale : Equivalent function without the estimator API.\n",
      " |  \n",
      " |  Notes\n",
      " |  -----\n",
      " |  NaNs are treated as missing values: disregarded in fit, and maintained in\n",
      " |  transform.\n",
      " |  \n",
      " |  For a comparison of the different scalers, transformers, and normalizers,\n",
      " |  see :ref:`examples/preprocessing/plot_all_scaling.py\n",
      " |  <sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  >>> from sklearn.preprocessing import MinMaxScaler\n",
      " |  >>> data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]\n",
      " |  >>> scaler = MinMaxScaler()\n",
      " |  >>> print(scaler.fit(data))\n",
      " |  MinMaxScaler()\n",
      " |  >>> print(scaler.data_max_)\n",
      " |  [ 1. 18.]\n",
      " |  >>> print(scaler.transform(data))\n",
      " |  [[0.   0.  ]\n",
      " |   [0.25 0.25]\n",
      " |   [0.5  0.5 ]\n",
      " |   [1.   1.  ]]\n",
      " |  >>> print(scaler.transform([[2, 2]]))\n",
      " |  [[1.5 0. ]]\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      MinMaxScaler\n",
      " |      sklearn.base._OneToOneFeatureMixin\n",
      " |      sklearn.base.TransformerMixin\n",
      " |      sklearn.base.BaseEstimator\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, feature_range=(0, 1), *, copy=True, clip=False)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  fit(self, X, y=None)\n",
      " |      Compute the minimum and maximum to be used for later scaling.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          The data used to compute the per-feature minimum and maximum\n",
      " |          used for later scaling along the features axis.\n",
      " |      \n",
      " |      y : None\n",
      " |          Ignored.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : object\n",
      " |          Fitted scaler.\n",
      " |  \n",
      " |  inverse_transform(self, X)\n",
      " |      Undo the scaling of X according to feature_range.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          Input data that will be transformed. It cannot be sparse.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      Xt : ndarray of shape (n_samples, n_features)\n",
      " |          Transformed data.\n",
      " |  \n",
      " |  partial_fit(self, X, y=None)\n",
      " |      Online computation of min and max on X for later scaling.\n",
      " |      \n",
      " |      All of X is processed as a single batch. This is intended for cases\n",
      " |      when :meth:`fit` is not feasible due to very large number of\n",
      " |      `n_samples` or because X is read from a continuous stream.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          The data used to compute the mean and standard deviation\n",
      " |          used for later scaling along the features axis.\n",
      " |      \n",
      " |      y : None\n",
      " |          Ignored.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : object\n",
      " |          Fitted scaler.\n",
      " |  \n",
      " |  transform(self, X)\n",
      " |      Scale features of X according to feature_range.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          Input data that will be transformed.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      Xt : ndarray of shape (n_samples, n_features)\n",
      " |          Transformed data.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base._OneToOneFeatureMixin:\n",
      " |  \n",
      " |  get_feature_names_out(self, input_features=None)\n",
      " |      Get output feature names for transformation.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      input_features : array-like of str or None, default=None\n",
      " |          Input features.\n",
      " |      \n",
      " |          - If `input_features` is `None`, then `feature_names_in_` is\n",
      " |            used as feature names in. If `feature_names_in_` is not defined,\n",
      " |            then names are generated: `[x0, x1, ..., x(n_features_in_)]`.\n",
      " |          - If `input_features` is an array-like, then `input_features` must\n",
      " |            match `feature_names_in_` if `feature_names_in_` is defined.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      feature_names_out : ndarray of str objects\n",
      " |          Same as input features.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from sklearn.base._OneToOneFeatureMixin:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.TransformerMixin:\n",
      " |  \n",
      " |  fit_transform(self, X, y=None, **fit_params)\n",
      " |      Fit to data, then transform it.\n",
      " |      \n",
      " |      Fits transformer to `X` and `y` with optional parameters `fit_params`\n",
      " |      and returns a transformed version of `X`.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          Input samples.\n",
      " |      \n",
      " |      y :  array-like of shape (n_samples,) or (n_samples, n_outputs),                 default=None\n",
      " |          Target values (None for unsupervised transformations).\n",
      " |      \n",
      " |      **fit_params : dict\n",
      " |          Additional fit parameters.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      X_new : ndarray array of shape (n_samples, n_features_new)\n",
      " |          Transformed array.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __getstate__(self)\n",
      " |  \n",
      " |  __repr__(self, N_CHAR_MAX=700)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setstate__(self, state)\n",
      " |  \n",
      " |  get_params(self, deep=True)\n",
      " |      Get parameters for this estimator.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      deep : bool, default=True\n",
      " |          If True, will return the parameters for this estimator and\n",
      " |          contained subobjects that are estimators.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      params : dict\n",
      " |          Parameter names mapped to their values.\n",
      " |  \n",
      " |  set_params(self, **params)\n",
      " |      Set the parameters of this estimator.\n",
      " |      \n",
      " |      The method works on simple estimators as well as on nested objects\n",
      " |      (such as :class:`~sklearn.pipeline.Pipeline`). The latter have\n",
      " |      parameters of the form ``<component>__<parameter>`` so that it's\n",
      " |      possible to update each component of a nested object.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      **params : dict\n",
      " |          Estimator parameters.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : estimator instance\n",
      " |          Estimator instance.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(MinMaxScaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "56c04a1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler() # !st step for transformation i.e normalization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "df9a85af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinMaxScaler()"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "17244b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = scaler.transform(X_train) # final step for transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1a778e23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "825415bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "db96a0aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0186515935232023"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "600e7569",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.74046017, 0.32583248],\n",
       "       [0.43166001, 0.2555088 ],\n",
       "       [0.18468554, 0.70500664],\n",
       "       ...,\n",
       "       [0.54913363, 0.79933822],\n",
       "       [0.2834197 , 0.38818708],\n",
       "       [0.56282703, 0.42371827]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cc72d219",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[?25hCollecting urllib3<1.27,>=1.21.1\n",
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      "\u001b[?25hCollecting certifi>=2017.4.17\n",
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      "\u001b[?25hCollecting zipp>=0.5\n",
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      "\u001b[?25hCollecting oauthlib>=3.0.0\n",
      "  Downloading oauthlib-3.2.0-py3-none-any.whl (151 kB)\n",
      "     |████████████████████████████████| 151 kB 4.9 MB/s            \n",
      "\u001b[?25hBuilding wheels for collected packages: termcolor\n",
      "  Building wheel for termcolor (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for termcolor: filename=termcolor-1.1.0-py3-none-any.whl size=4848 sha256=190a4c1e96158c239b7446f84707f88ccd6af31091605853bc34f71f037fc144\n",
      "  Stored in directory: /Users/fasihussaini/Library/Caches/pip/wheels/b6/0d/90/0d1bbd99855f99cb2f6c2e5ff96f8023fad8ec367695f7d72d\n",
      "Successfully built termcolor\n",
      "Installing collected packages: urllib3, pyasn1, idna, charset-normalizer, certifi, zipp, rsa, requests, pyasn1-modules, oauthlib, cachetools, requests-oauthlib, importlib-metadata, google-auth, werkzeug, tensorboard-plugin-wit, tensorboard-data-server, protobuf, markdown, grpcio, google-auth-oauthlib, absl-py, wrapt, tf-estimator-nightly, termcolor, tensorflow-io-gcs-filesystem, tensorboard, opt-einsum, libclang, keras-preprocessing, keras, h5py, google-pasta, gast, flatbuffers, astunparse, tensorflow\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33m  DEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n",
      "\u001b[33mDEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully installed absl-py-1.0.0 astunparse-1.6.3 cachetools-5.0.0 certifi-2021.10.8 charset-normalizer-2.0.12 flatbuffers-2.0 gast-0.5.3 google-auth-2.6.0 google-auth-oauthlib-0.4.6 google-pasta-0.2.0 grpcio-1.44.0 h5py-3.6.0 idna-3.3 importlib-metadata-4.11.1 keras-2.8.0 keras-preprocessing-1.1.2 libclang-13.0.0 markdown-3.3.6 oauthlib-3.2.0 opt-einsum-3.3.0 protobuf-3.19.4 pyasn1-0.4.8 pyasn1-modules-0.2.8 requests-2.27.1 requests-oauthlib-1.3.1 rsa-4.8 tensorboard-2.8.0 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 tensorflow-2.8.0 tensorflow-io-gcs-filesystem-0.24.0 termcolor-1.1.0 tf-estimator-nightly-2.8.0.dev2021122109 urllib3-1.26.8 werkzeug-2.0.3 wrapt-1.13.3 zipp-3.7.0\n",
      "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.0.3 is available.\n",
      "You should consider upgrading via the '/usr/local/opt/python@3.9/bin/python3.9 -m pip install --upgrade pip' command.\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a82ec91c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential \n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "03674ae5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7a53c1a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dense(4,activation='relu'))\n",
    "model.add(Dense(4,activation='relu'))\n",
    "model.add(Dense(4,activation='relu'))\n",
    "\n",
    "model.add(Dense(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "68ad88f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='rmsprop', loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "66f86c21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n",
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      "Epoch 10/250\n",
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      "Epoch 12/250\n",
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      "Epoch 13/250\n",
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      "Epoch 14/250\n",
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      "Epoch 15/250\n",
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      "Epoch 17/250\n",
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      "Epoch 18/250\n",
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      "Epoch 19/250\n",
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      "Epoch 21/250\n",
      "22/22 [==============================] - 0s 806us/step - loss: 253794.4062\n",
      "Epoch 22/250\n",
      "22/22 [==============================] - 0s 781us/step - loss: 253470.5781\n",
      "Epoch 23/250\n",
      "22/22 [==============================] - 0s 846us/step - loss: 253117.5469\n",
      "Epoch 24/250\n",
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      "Epoch 30/250\n",
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      "Epoch 31/250\n",
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      "Epoch 34/250\n",
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      "Epoch 35/250\n",
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      "Epoch 36/250\n",
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      "Epoch 37/250\n",
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      "Epoch 38/250\n",
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      "Epoch 39/250\n",
      "22/22 [==============================] - 0s 820us/step - loss: 242426.1875\n",
      "Epoch 40/250\n",
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      "Epoch 41/250\n",
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      "Epoch 42/250\n",
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      "Epoch 43/250\n",
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      "Epoch 44/250\n",
      "22/22 [==============================] - 0s 816us/step - loss: 236438.8125\n",
      "Epoch 45/250\n",
      "22/22 [==============================] - 0s 819us/step - loss: 235048.8281\n",
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      "Epoch 47/250\n",
      "22/22 [==============================] - 0s 730us/step - loss: 232059.6094\n",
      "Epoch 48/250\n",
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      "Epoch 49/250\n",
      "22/22 [==============================] - 0s 839us/step - loss: 228776.2812\n",
      "Epoch 50/250\n",
      "22/22 [==============================] - 0s 822us/step - loss: 227025.8125\n",
      "Epoch 51/250\n",
      "22/22 [==============================] - 0s 790us/step - loss: 225196.2969\n",
      "Epoch 52/250\n",
      "22/22 [==============================] - 0s 831us/step - loss: 223284.5000\n",
      "Epoch 53/250\n",
      "22/22 [==============================] - 0s 804us/step - loss: 221283.4219\n",
      "Epoch 54/250\n",
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      "Epoch 55/250\n",
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     "text": [
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     ]
    },
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     "text": [
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x13d135d30>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x=X_train,y=y_train,epochs=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4e7b9baa",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_df = pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3fba19e2",
   "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>loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>256712.890625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>256648.609375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>256571.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>256501.968750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>256428.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>28.491323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>27.611370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>27.437950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>26.914288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>26.469685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              loss\n",
       "0    256712.890625\n",
       "1    256648.609375\n",
       "2    256571.937500\n",
       "3    256501.968750\n",
       "4    256428.500000\n",
       "..             ...\n",
       "245      28.491323\n",
       "246      27.611370\n",
       "247      27.437950\n",
       "248      26.914288\n",
       "249      26.469685\n",
       "\n",
       "[250 rows x 1 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5b8df646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "addfb655",
   "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>loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>256712.890625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>256648.609375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>256571.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>256501.968750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>256428.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>28.491323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>27.611370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>27.437950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>26.914288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>26.469685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              loss\n",
       "0    256712.890625\n",
       "1    256648.609375\n",
       "2    256571.937500\n",
       "3    256501.968750\n",
       "4    256428.500000\n",
       "..             ...\n",
       "245      28.491323\n",
       "246      27.611370\n",
       "247      27.437950\n",
       "248      26.914288\n",
       "249      26.469685\n",
       "\n",
       "[250 rows x 1 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "96ce55f6",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'x_test' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [62]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mx_test\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'x_test' is not defined"
     ]
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8d4c0220",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.39533339,  0.38540671],\n",
       "       [ 0.57300502,  0.74070924],\n",
       "       [ 0.61294076,  0.65734264],\n",
       "       [ 0.38169528,  0.73076311],\n",
       "       [ 0.07776591,  0.46323644],\n",
       "       [ 0.49421971,  0.68949258],\n",
       "       [ 0.2065742 ,  0.70096247],\n",
       "       [ 0.55141988,  0.41776773],\n",
       "       [ 0.36812191,  0.69082394],\n",
       "       [ 0.56465724,  0.3877465 ],\n",
       "       [ 0.57048029,  0.71810824],\n",
       "       [ 0.79569525,  0.47979901],\n",
       "       [ 0.38488235,  0.41856516],\n",
       "       [ 0.33142545,  0.42411972],\n",
       "       [ 0.89361377,  0.63912775],\n",
       "       [ 0.50191166,  0.39789174],\n",
       "       [ 0.7159808 ,  0.43688384],\n",
       "       [ 0.35812192,  0.92028225],\n",
       "       [ 0.49989367,  0.85589311],\n",
       "       [ 0.63965434,  0.59024789],\n",
       "       [ 0.19053626,  0.34203634],\n",
       "       [ 0.40982896,  0.45849208],\n",
       "       [ 0.26844791,  0.40167269],\n",
       "       [ 0.38649709,  0.33592759],\n",
       "       [ 0.50654703,  0.65789843],\n",
       "       [ 0.41260556,  0.79338075],\n",
       "       [ 0.45631316,  0.61323307],\n",
       "       [ 0.44064792,  0.40889315],\n",
       "       [ 0.42963904,  0.87598452],\n",
       "       [ 0.5145433 ,  0.34457637],\n",
       "       [ 0.53589468,  0.39199459],\n",
       "       [ 0.46139966,  0.51485328],\n",
       "       [ 0.44369075,  0.42880573],\n",
       "       [ 0.49063994,  0.89952766],\n",
       "       [ 0.59785881,  0.32523307],\n",
       "       [ 0.28446406,  0.4651156 ],\n",
       "       [ 0.77957933,  0.39234978],\n",
       "       [ 0.53706097,  0.61018179],\n",
       "       [ 0.25989698,  0.66393142],\n",
       "       [ 0.11552588,  0.50295721],\n",
       "       [ 0.6318509 ,  0.70171257],\n",
       "       [ 0.48801329,  0.36279903],\n",
       "       [ 0.60976949,  0.68389143],\n",
       "       [ 0.63095152,  0.35195791],\n",
       "       [ 0.46866324,  0.54548323],\n",
       "       [ 0.85019104,  0.51945821],\n",
       "       [ 0.83979528,  0.70163971],\n",
       "       [ 0.57599467,  0.46915954],\n",
       "       [ 0.45446128,  0.18040915],\n",
       "       [ 0.43326779,  0.52930506],\n",
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       "       [ 0.53929518,  0.33822417],\n",
       "       [ 0.2903287 ,  0.44841353]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "42c68bc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       502.90947332, 612.72791033, 417.56972453, 410.53824963])"
      ]
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     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b880c0e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.481943130493164"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test,y_test,verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e9e94549",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 0s 818us/step - loss: 26.9457\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "26.945728302001953"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f1eb8800",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = model.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "dce4a8b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "test_predictions.shape\n",
    "test_predictions = pd.Series(test_predictions.reshape(300,))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "5b3ad969",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df = pd.DataFrame(y_test,columns=['Test True Y'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3916c46c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df = pd.concat([pred_df,test_predictions],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "102656d0",
   "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>Test True Y</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>402.296319</td>\n",
       "      <td>406.203400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>624.156198</td>\n",
       "      <td>621.177795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>582.455066</td>\n",
       "      <td>589.950317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>578.588606</td>\n",
       "      <td>571.298645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>371.224104</td>\n",
       "      <td>369.381317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>525.704657</td>\n",
       "      <td>528.668396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>502.909473</td>\n",
       "      <td>516.668030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>612.727910</td>\n",
       "      <td>607.354187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>417.569725</td>\n",
       "      <td>417.096405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>410.538250</td>\n",
       "      <td>412.192261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Test True Y           0\n",
       "0     402.296319  406.203400\n",
       "1     624.156198  621.177795\n",
       "2     582.455066  589.950317\n",
       "3     578.588606  571.298645\n",
       "4     371.224104  369.381317\n",
       "..           ...         ...\n",
       "295   525.704657  528.668396\n",
       "296   502.909473  516.668030\n",
       "297   612.727910  607.354187\n",
       "298   417.569725  417.096405\n",
       "299   410.538250  412.192261\n",
       "\n",
       "[300 rows x 2 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "68268ed7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df.columns = ['Test True Y','Model Predictions']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "8d721e13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Test True Y', ylabel='Model Predictions'>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='Test True Y', y='Model Predictions', data = pred_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "e95fdf51",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error,mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e2e727c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.262484179339338"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(pred_df['Test True Y'],pred_df['Model Predictions'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "d65cf848",
   "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>price</th>\n",
       "      <th>feature1</th>\n",
       "      <th>feature2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>498.673029</td>\n",
       "      <td>1000.014171</td>\n",
       "      <td>999.979847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>93.785431</td>\n",
       "      <td>0.974018</td>\n",
       "      <td>0.948330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>223.346793</td>\n",
       "      <td>997.058347</td>\n",
       "      <td>996.995651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>433.025732</td>\n",
       "      <td>999.332068</td>\n",
       "      <td>999.316106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>502.382117</td>\n",
       "      <td>1000.009915</td>\n",
       "      <td>1000.002243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>564.921588</td>\n",
       "      <td>1000.637580</td>\n",
       "      <td>1000.645380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>774.407854</td>\n",
       "      <td>1003.207934</td>\n",
       "      <td>1002.666308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             price     feature1     feature2\n",
       "count  1000.000000  1000.000000  1000.000000\n",
       "mean    498.673029  1000.014171   999.979847\n",
       "std      93.785431     0.974018     0.948330\n",
       "min     223.346793   997.058347   996.995651\n",
       "25%     433.025732   999.332068   999.316106\n",
       "50%     502.382117  1000.009915  1000.002243\n",
       "75%     564.921588  1000.637580  1000.645380\n",
       "max     774.407854  1003.207934  1002.666308"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "c88f1256",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.242322982232593"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(pred_df['Test True Y'],pred_df['Model Predictions'])**0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "39f52c8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_gem = [[998,1000]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "0ca193de",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_gem=scaler.transform(new_gem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "0f7eec5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.14117652, 0.53968792]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_gem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "f5925078",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[421.5652]], dtype=float32)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(new_gem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "3352db85",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "121d1b9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('my_gem_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5377df0f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
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    "name": "ipython",
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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