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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import datasets"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "id": "90dfc907-e19f-43c9-a560-9dda28229244",
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      "/Users/zijun/Documents/anaconda3/envs/class/lib/python3.10/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
      "\n",
      "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
      "    the documentation of this function for further details.\n",
      "\n",
      "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
      "    dataset unless the purpose of the code is to study and educate about\n",
      "    ethical issues in data science and machine learning.\n",
      "\n",
      "    In this special case, you can fetch the dataset from the original\n",
      "    source::\n",
      "\n",
      "        import pandas as pd\n",
      "        import numpy as np\n",
      "\n",
      "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
      "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
      "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
      "        target = raw_df.values[1::2, 2]\n",
      "\n",
      "    Alternative datasets include the California housing dataset (i.e.\n",
      "    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
      "    dataset. You can load the datasets as follows::\n",
      "\n",
      "        from sklearn.datasets import fetch_california_housing\n",
      "        housing = fetch_california_housing()\n",
      "\n",
      "    for the California housing dataset and::\n",
      "\n",
      "        from sklearn.datasets import fetch_openml\n",
      "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
      "\n",
      "    for the Ames housing dataset.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
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   "source": [
    "## import house price data from sklearn datasets\n",
    "boston = datasets.load_boston()\n",
    "names = boston.feature_names\n",
    "\n",
    "bos = pd.DataFrame(boston.data)\n",
    "bos.columns = names\n",
    "bos.head() # display the first five rows"
   ]
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       "\n",
       "   PTRATIO       B  LSTAT  PRICE  \n",
       "0     15.3  396.90   4.98   24.0  \n",
       "1     17.8  396.90   9.14   21.6  \n",
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       "3     18.7  394.63   2.94   33.4  \n",
       "4     18.7  396.90   5.33   36.2  "
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "target = boston.target  # price variable we want to predict\n",
    "bos['PRICE'] = target #create a new column with target\n",
    "bos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f5205c1-7e27-4097-88c0-0d13c119ada6",
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    {
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     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston house prices dataset\n",
      "---------------------------\n",
      "\n",
      "**Data Set Characteristics:**  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of black people by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      ".. topic:: References\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(boston.DESCR)"
   ]
  },
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(outputEl, {childList: true});\n",
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     "output_type": "display_data"
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   ],
   "source": [
    "from plotly.offline import init_notebook_mode, iplot\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "# Initialize Plotly in offline mode\n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "trace = go.Scatter(x = bos.RM, y=target, mode='markers')\n",
    "\n",
    "# Define layout with axis labels\n",
    "layout = go.Layout(\n",
    "    title=\"Scatter Plot of RM vs Target\",\n",
    "    xaxis=dict(title=\"Average Number of Rooms (RM)\"),\n",
    "    yaxis=dict(title=\"Target Variable\")\n",
    ")\n",
    "\n",
    "# Combine trace and layout\n",
    "fig = go.Figure(data=[trace], layout=layout)\n",
    "\n",
    "# Display the plot\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "372df762-bc7b-443d-947f-adbaef00df7d",
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.067815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.314815</td>\n",
       "      <td>0.577505</td>\n",
       "      <td>0.641607</td>\n",
       "      <td>0.269203</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.208015</td>\n",
       "      <td>0.287234</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.547998</td>\n",
       "      <td>0.782698</td>\n",
       "      <td>0.348962</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.104962</td>\n",
       "      <td>0.553191</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.204470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.694386</td>\n",
       "      <td>0.599382</td>\n",
       "      <td>0.348962</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.104962</td>\n",
       "      <td>0.553191</td>\n",
       "      <td>0.989737</td>\n",
       "      <td>0.063466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.658555</td>\n",
       "      <td>0.441813</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.648936</td>\n",
       "      <td>0.994276</td>\n",
       "      <td>0.033389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000705</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.687105</td>\n",
       "      <td>0.528321</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.648936</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.099338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CRIM    ZN     INDUS  CHAS       NOX        RM       AGE       DIS  \\\n",
       "0  0.000000  0.18  0.067815   0.0  0.314815  0.577505  0.641607  0.269203   \n",
       "1  0.000236  0.00  0.242302   0.0  0.172840  0.547998  0.782698  0.348962   \n",
       "2  0.000236  0.00  0.242302   0.0  0.172840  0.694386  0.599382  0.348962   \n",
       "3  0.000293  0.00  0.063050   0.0  0.150206  0.658555  0.441813  0.448545   \n",
       "4  0.000705  0.00  0.063050   0.0  0.150206  0.687105  0.528321  0.448545   \n",
       "\n",
       "        RAD       TAX   PTRATIO         B     LSTAT  \n",
       "0  0.000000  0.208015  0.287234  1.000000  0.089680  \n",
       "1  0.043478  0.104962  0.553191  1.000000  0.204470  \n",
       "2  0.043478  0.104962  0.553191  0.989737  0.063466  \n",
       "3  0.086957  0.066794  0.648936  0.994276  0.033389  \n",
       "4  0.086957  0.066794  0.648936  1.000000  0.099338  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as the number of rooms increase, the price of the house increases\n",
    "\n",
    "# perform a linear regression\n",
    "# normalizing\n",
    "from sklearn import preprocessing\n",
    "\n",
    "bos_new = bos\n",
    "del bos_new['PRICE']\n",
    "val = bos_new.values\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "val_scaled = min_max_scaler.fit_transform(val)\n",
    "bos_norm = pd.DataFrame(val_scaled,columns=bos.columns)\n",
    "bos_norm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0e8daa22-6e32-4fcf-81d7-49d51c1aa3d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X= pd.DataFrame(bos_norm['RM'],columns=['RM'])\n",
    "y= target #This is the price column\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6603a9c6-ec69-47fa-8e94-ed9d12e26d19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "lr = linear_model.LinearRegression() \n",
    "lr.fit(X_train,y_train) #Fit the model on the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2e197afa-b1dc-400e-b734-b347c5ebb525",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.4</td>\n",
       "      <td>25.179933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.1</td>\n",
       "      <td>26.822325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.5</td>\n",
       "      <td>22.270042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.6</td>\n",
       "      <td>28.063045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.6</td>\n",
       "      <td>14.361567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.3</td>\n",
       "      <td>23.573245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>50.0</td>\n",
       "      <td>32.972370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.3</td>\n",
       "      <td>16.441335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20.7</td>\n",
       "      <td>18.967406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>37.6</td>\n",
       "      <td>37.890620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20.4</td>\n",
       "      <td>20.815097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>27.5</td>\n",
       "      <td>27.527483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>36.2</td>\n",
       "      <td>20.966840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>32.0</td>\n",
       "      <td>27.304331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>33.1</td>\n",
       "      <td>27.054402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>48.8</td>\n",
       "      <td>41.086144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>24.6</td>\n",
       "      <td>23.528615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>26.4</td>\n",
       "      <td>16.146776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>23.2</td>\n",
       "      <td>19.860010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>17.0</td>\n",
       "      <td>20.832949</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual  Predicted\n",
       "0     28.4  25.179933\n",
       "1     31.1  26.822325\n",
       "2     23.5  22.270042\n",
       "3     26.6  28.063045\n",
       "4     19.6  14.361567\n",
       "5     14.3  23.573245\n",
       "6     50.0  32.972370\n",
       "7     14.3  16.441335\n",
       "8     20.7  18.967406\n",
       "9     37.6  37.890620\n",
       "10    20.4  20.815097\n",
       "11    27.5  27.527483\n",
       "12    36.2  20.966840\n",
       "13    32.0  27.304331\n",
       "14    33.1  27.054402\n",
       "15    48.8  41.086144\n",
       "16    24.6  23.528615\n",
       "17    26.4  16.146776\n",
       "18    23.2  19.860010\n",
       "19    17.0  20.832949"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predict price on test data\n",
    "predicted = lr.predict(X_test)\n",
    "lr_predictions = pd.DataFrame({'Actual':y_test,'Predicted':predicted})\n",
    "lr_predictions[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "de350b71-0b47-409f-9eed-9d0df4179096",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of model: 0.513277\n",
      "Mean Squared Error: 48.520361\n"
     ]
    }
   ],
   "source": [
    "#Actual vs predicted score\n",
    "# coefficient of determination of the prediction\n",
    "# R2 = (1-u/v), where \n",
    "# u is the residual sum of squares ((y_true - y_pred)** 2).sum() \n",
    "# v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum()\n",
    "# the higher the better; the highest/best possible value is 1\n",
    "score = lr.score(X_test,y_test) \n",
    "\n",
    "#Mean squared error\n",
    "mseFull = np.mean((y_test - lr.predict(X_test))**2)\n",
    "\n",
    "print(\"Accuracy of model: %f\" % score)\n",
    "print(\"Mean Squared Error: %f\" % mseFull)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b6a977bf-4e77-451d-bcc0-80da390e5a4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_graph(df,title):\n",
    "    #Plot of linear regerssion\n",
    "    trace = go.Scatter(x=df['Actual'].values,\n",
    "                       y=df['Predicted'].values,\n",
    "                       mode ='markers')\n",
    "    \n",
    "    trace2 = go.Scatter(x = df['Actual'].values,\n",
    "                        y = df['Actual'].values,\n",
    "                        mode = 'lines',\n",
    "                        name = 'lines'\n",
    "                        )\n",
    "\n",
    "    layout = go.Layout(\n",
    "        title=\"%s\" % title,\n",
    "        xaxis=dict(\n",
    "            title='Actual',\n",
    "            titlefont=dict(\n",
    "                family='Courier New, monospace',\n",
    "                size=18,\n",
    "                color='#7f7f7f'\n",
    "            )\n",
    "        ),\n",
    "        yaxis=dict(\n",
    "            title='Predicted',\n",
    "            titlefont=dict(\n",
    "                family='Courier New, monospace',\n",
    "                size=18,\n",
    "                color='#7f7f7f'\n",
    "            )\n",
    "        )\n",
    "    )\n",
    "    data = [trace,trace2]\n",
    "    fig = go.Figure(data=data,layout=layout)\n",
    "    return iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
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",
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   ],
   "source": [
    "draw_graph(lr_predictions,'Linear Regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ddf6c082-2008-4a6b-9f6b-df6296ebdf35",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Multiple Linear Regression\n",
    "# A multiple linear regression is used to predict a target variable Y using two or more independent variables X1,X2,X3...\n",
    "\n",
    "X= bos_norm\n",
    "y= target #This is the price column\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=10)"
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   "execution_count": 22,
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr = linear_model.LinearRegression() \n",
    "#Fit the model on the training data\n",
    "mlr.fit(X_train,y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4786e9a4-98bb-48f4-8ecb-67b4931bdac2",
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
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       "    Actual  Predicted\n",
       "0     28.4  31.114396\n",
       "1     31.1  31.820602\n",
       "2     23.5  30.556206\n",
       "3     26.6  22.440421\n",
       "4     19.6  18.803988\n",
       "5     14.3  16.276253\n",
       "6     50.0  36.135344\n",
       "7     14.3  14.624633\n",
       "8     20.7  24.561962\n",
       "9     37.6  37.279617\n",
       "10    20.4  21.291084\n",
       "11    27.5  30.612582\n",
       "12    36.2  27.948888\n",
       "13    32.0  33.806971\n",
       "14    33.1  33.250723\n",
       "15    48.8  40.771778\n",
       "16    24.6  24.317320\n",
       "17    26.4  23.297732\n",
       "18    23.2  25.507320\n",
       "19    17.0  21.089598"
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     "execution_count": 23,
     "metadata": {},
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   ],
   "source": [
    "#Predict price on test data \n",
    "predicted = mlr.predict(X_test)\n",
    "mlr_predictions = pd.DataFrame({'Actual':y_test,'Predicted':predicted})\n",
    "mlr_predictions[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "66d5140c-2d2c-445a-8330-6188c0e34b49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of model: 0.674559\n",
      "Mean Squared Error: 32.442537\n"
     ]
    }
   ],
   "source": [
    "#Actual vs predicted score\n",
    "score = mlr.score(X_test,y_test)\n",
    "#Mean squared error\n",
    "mseFull = np.mean((y_test - mlr.predict(X_test))**2)\n",
    "\n",
    "print(\"Accuracy of model: %f\" % score)\n",
    "print(\"Mean Squared Error: %f\" % mseFull)"
   ]
  },
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   "execution_count": 25,
   "id": "3bc48402-0854-4315-a346-cefc613194b8",
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",
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      "/Users/zijun/Documents/anaconda3/envs/class/lib/python3.10/site-packages/sklearn/tree/_classes.py:306: FutureWarning:\n",
      "\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0'`.\n",
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       "      <td>27.5</td>\n",
       "      <td>19.883333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>36.2</td>\n",
       "      <td>22.185714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>32.0</td>\n",
       "      <td>32.775000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>33.1</td>\n",
       "      <td>32.775000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>48.8</td>\n",
       "      <td>46.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>24.6</td>\n",
       "      <td>24.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>26.4</td>\n",
       "      <td>24.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>23.2</td>\n",
       "      <td>18.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>17.0</td>\n",
       "      <td>14.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual  Predicted\n",
       "0     28.4  23.060000\n",
       "1     31.1  32.775000\n",
       "2     23.5  23.475000\n",
       "3     26.6  26.785714\n",
       "4     19.6  18.775000\n",
       "5     14.3  16.171429\n",
       "6     50.0  43.225000\n",
       "7     14.3  16.400000\n",
       "8     20.7  20.185714\n",
       "9     37.6  49.750000\n",
       "10    20.4  19.883333\n",
       "11    27.5  19.883333\n",
       "12    36.2  22.185714\n",
       "13    32.0  32.775000\n",
       "14    33.1  32.775000\n",
       "15    48.8  46.200000\n",
       "16    24.6  24.450000\n",
       "17    26.4  24.080000\n",
       "18    23.2  18.350000\n",
       "19    17.0  14.250000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision tree algorithms are used in a similar way to linear regression with the end goal to predict a target variable. \n",
    "# Decision Trees are much better at predicting non-linear relationships but can also suffer from over fitting\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "dtr = DecisionTreeRegressor(random_state = 10,\n",
    "                            max_depth=13, \n",
    "                            min_samples_leaf=4,\n",
    "                            min_samples_split=4,\n",
    "                            max_features = 'auto')\n",
    "\n",
    "\n",
    "dtr.fit(X_train, y_train)\n",
    "\n",
    "predicted = dtr.predict(X_test)\n",
    "\n",
    "dtr_predictions = pd.DataFrame({'Actual':y_test,'Predicted':predicted})\n",
    "\n",
    "dtr_predictions[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a362d2a1-401f-4e2d-98c1-115ea9ce7b7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of model: 0.746294\n",
      "Mean Squared Error: 25.291414\n"
     ]
    }
   ],
   "source": [
    "#Model score \n",
    "score = dtr.score(X_test,y_test)\n",
    "mseFull = np.mean((y_test - predicted)**2)\n",
    "\n",
    "print(\"Accuracy of model: %f\" % score)\n",
    "print(\"Mean Squared Error: %f\" % mseFull)"
   ]
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      "Best score: 0.7758767442793008\n",
      "Best parameters: {'max_depth': 6, 'min_samples_leaf': 2, 'min_samples_split': 6}\n",
      "predicted score against actual: 0.762442\n",
      "Mean Squared Error: 23.681619\n"
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       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
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       "      <th>0</th>\n",
       "      <td>28.4</td>\n",
       "      <td>22.063793</td>\n",
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       "      <th>1</th>\n",
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       "      <td>23.5</td>\n",
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       "      <th>3</th>\n",
       "      <td>26.6</td>\n",
       "      <td>27.162500</td>\n",
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       "      <th>4</th>\n",
       "      <td>19.6</td>\n",
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       "   Actual  Predicted\n",
       "0    28.4  22.063793\n",
       "1    31.1  30.609091\n",
       "2    23.5  23.755172\n",
       "3    26.6  27.162500\n",
       "4    19.6  20.043333"
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   "source": [
    "import warnings\n",
    "\n",
    "# Suppress warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "dtr = DecisionTreeRegressor(random_state=10, \n",
    "                            max_depth=13, \n",
    "                            max_features='auto')\n",
    "\n",
    "params = {'min_samples_split': range(2, 10, 2),\n",
    "          'min_samples_leaf': range(2, 10, 2),\n",
    "          'max_depth': range(1, 13)}\n",
    "    \n",
    "grid_search = GridSearchCV(dtr, param_grid=params, cv=10)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))\n",
    "\n",
    "predicted = grid_search.predict(X_test)\n",
    "\n",
    "mseFull = np.mean((y_test - predicted)**2)\n",
    "\n",
    "grid_predictions = pd.DataFrame({'Actual': y_test, 'Predicted': predicted})\n",
    "score = grid_search.score(X_test, y_test)\n",
    "print(\"predicted score against actual: %f\" % score)\n",
    "print(\"Mean Squared Error: %f\" % mseFull)\n",
    "\n",
    "grid_predictions.head()"
   ]
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            [
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           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
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             "#7201a8"
            ],
            [
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             "#9c179e"
            ],
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             "#bd3786"
            ],
            [
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             "#d8576b"
            ],
            [
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             "#ed7953"
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             "#fb9f3a"
            ],
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             "#fdca26"
            ],
            [
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             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "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",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Decision Grid Search Regression"
        },
        "xaxis": {
         "autorange": true,
         "range": [
          4.392748324192565,
          52.60725167580743
         ],
         "title": {
          "font": {
           "color": "#7f7f7f",
           "family": "Courier New, monospace",
           "size": 18
          },
          "text": "Actual"
         },
         "type": "linear"
        },
        "yaxis": {
         "autorange": true,
         "range": [
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          53.302403846153844
         ],
         "title": {
          "font": {
           "color": "#7f7f7f",
           "family": "Courier New, monospace",
           "size": 18
          },
          "text": "Predicted"
         },
         "type": "linear"
        }
       }
      },
      "image/png": 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",
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   "source": [
    "draw_graph(grid_predictions,'Decision Grid Search Regression')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 34,
   "id": "96909503-bc76-49fa-b742-5b6affb5d616",
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    {
     "name": "stdout",
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     "text": [
      "predicted score against actual: 0.834797\n"
     ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.4</td>\n",
       "      <td>25.333491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.1</td>\n",
       "      <td>29.497785</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.5</td>\n",
       "      <td>26.325478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.6</td>\n",
       "      <td>28.452893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.6</td>\n",
       "      <td>20.946747</td>\n",
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      "text/plain": [
       "   Actual  Predicted\n",
       "0    28.4  25.333491\n",
       "1    31.1  29.497785\n",
       "2    23.5  26.325478\n",
       "3    26.6  28.452893\n",
       "4    19.6  20.946747"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#Random Forest\n",
    "#A Random Forest as the name suggests is a collection of decision trees. \n",
    "#It randomly subsets the data and feeds it into decision trees, also sometimes referred to as 'bagging'. \n",
    "#This is particularly useful as decision trees are prone to overfitting\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf = RandomForestRegressor(n_estimators= 100, random_state = 10,oob_score=True,\n",
    "                           max_depth=7, min_samples_leaf=3,min_samples_split=9,n_jobs=-1)\n",
    "\n",
    "\n",
    "rf.fit(X_train, y_train)\n",
    "predicted = rf.predict(X_test)\n",
    "rf_predictions = pd.DataFrame({'Actual':y_test,'Predicted':predicted})\n",
    "rf_score = rf.score(X_test,y_test)\n",
    "print(\"predicted score against actual: %f\" % rf_score)\n",
    "rf_predictions.head()"
   ]
  },
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   "execution_count": 35,
   "id": "e12bc6ce-e234-4e70-80fe-b303551eb08f",
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     "text": [
      "Best score: 0.8431507935229563\n",
      "Best parameters: {'max_depth': 12, 'min_samples_leaf': 1, 'min_samples_split': 2}\n",
      "predicted score against actual: 0.861802\n"
     ]
    },
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
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       "\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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.4</td>\n",
       "      <td>25.739281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.1</td>\n",
       "      <td>29.650133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.5</td>\n",
       "      <td>26.013621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.6</td>\n",
       "      <td>28.474700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.6</td>\n",
       "      <td>17.931524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0    28.4  25.739281\n",
       "1    31.1  29.650133\n",
       "2    23.5  26.013621\n",
       "3    26.6  28.474700\n",
       "4    19.6  17.931524"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params ={'max_depth':range(9,13),\n",
    "         'min_samples_leaf':range(1,3),\n",
    "         'min_samples_split':range(2,10,2)\n",
    "}\n",
    "grid_search = GridSearchCV(rf,param_grid=params,cv=10)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))\n",
    "\n",
    "predicted = grid_search.predict(X_test)\n",
    "\n",
    "grid_predictions = pd.DataFrame({'Actual':y_test,'Predicted':predicted})\n",
    "score = grid_search.score(X_test,y_test)\n",
    "print(\"predicted score against actual: %f\" % score)\n",
    "grid_predictions.head()"
   ]
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   ],
   "source": [
    "draw_graph(grid_predictions,'Random Forest Regression')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 37,
   "id": "6a8c550f-a100-40c8-97a2-a48b8a34f840",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted score against actual: 0.862393\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.4</td>\n",
       "      <td>25.104103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.1</td>\n",
       "      <td>29.815745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.5</td>\n",
       "      <td>25.429537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.6</td>\n",
       "      <td>28.376858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.6</td>\n",
       "      <td>21.289210</td>\n",
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      "text/plain": [
       "   Actual  Predicted\n",
       "0    28.4  25.104103\n",
       "1    31.1  29.815745\n",
       "2    23.5  25.429537\n",
       "3    26.6  28.376858\n",
       "4    19.6  21.289210"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Gradient Boosted Regression\n",
    "# Gradient boosted regression functions in a similar way to a random forest. \n",
    "# Instead of creating x number of decision trees and aggregating them \n",
    "# a boosted learning algorithm will perform the same basic process of building a learning algorithm on samples of data \n",
    "# but will assign a higher and higher weight to a hard to classify observation.\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "gbr = GradientBoostingRegressor(n_estimators= 100, \n",
    "                                random_state = 10,\n",
    "                                max_depth=12, \n",
    "                                min_samples_leaf=4,\n",
    "                                min_samples_split=4,\n",
    "                                learning_rate=0.2)\n",
    "\n",
    "gbr.fit(X_train, y_train)\n",
    "predicted = gbr.predict(X_test)\n",
    "gbr_predictions = pd.DataFrame({'Actual':y_test,'Predicted':predicted})\n",
    "gbr_score = gbr.score(X_test,y_test)\n",
    "print(\"predicted score against actual: %f\" % gbr_score)\n",
    "gbr_predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e92fdba3-fb90-4940-93dd-766183f70a1b",
   "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.8722425141137371\n",
      "Best parameters: {'learning_rate': 0.1, 'max_depth': 3, 'min_samples_leaf': 3, 'min_samples_split': 2}\n",
      "predicted score against actual: 0.891742\n"
     ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.4</td>\n",
       "      <td>26.288515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.1</td>\n",
       "      <td>29.454421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.5</td>\n",
       "      <td>25.595406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.6</td>\n",
       "      <td>26.926590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.6</td>\n",
       "      <td>19.326003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0    28.4  26.288515\n",
       "1    31.1  29.454421\n",
       "2    23.5  25.595406\n",
       "3    26.6  26.926590\n",
       "4    19.6  19.326003"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params ={'max_depth':range(1,5,2),\n",
    "         'min_samples_leaf':range(1,5,2),\n",
    "         'min_samples_split':range(2,4),\n",
    "         'learning_rate':[0.1]\n",
    "         }\n",
    "grid_search = GridSearchCV(gbr,param_grid=params,cv=10)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))\n",
    "\n",
    "predicted = grid_search.predict(X_test)\n",
    "\n",
    "grid_predictions = pd.DataFrame({'Actual':y_test,'Predicted':predicted})\n",
    "score = grid_search.score(X_test,y_test)\n",
    "print(\"predicted score against actual: %f\" % score)\n",
    "grid_predictions.head()"
   ]
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",
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