07. Data Analysis with Python/Final Assignment - House Price Predictions.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["<center>\n","    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/FinalModule_Coursera/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n","</center>\n","\n","<h1 align=center><font size = 5>Data Analysis with Python</font></h1>\n"]},{"cell_type":"markdown","metadata":{},"source":["# House Sales in King County, USA\n"]},{"cell_type":"markdown","metadata":{},"source":["This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015.\n"]},{"cell_type":"markdown","metadata":{},"source":["| Variable|  Description |\n","|---|---|\n","|id|A notation for a house|\n","|date|Date house was sold|\n","|price| Price is prediction target|\n","|bedrooms| Number of bedrooms|\n","|bathrooms| Number of bathrooms|\n","|sqft_living| Square footage of the home|\n","|sqft_lot| Square footage of the lot|\n","|floors| Total floors (levels) in house|\n","|waterfront|House which has a view to a waterfront|\n","|view|Has been viewed|\n","|condition|How good the condition is overall|\n","|grade|overall grade given to the housing unit, based on King County grading system|\n","|sqft_above|Square footage of house apart from basement|\n","|sqft_basement|Square footage of the basement|\n","|yr_built|Built Year|\n","|yr_renovated|Year when house was renovated|\n","|zipcode| Zip code|\n","|lat|Latitude coordinate|\n","|long| Longitude coordinate|\n","|sqft_living15|Living room area in 2015(implies-- some renovations) This might or might not have affected the lotsize area|\n","|sqft_lot15|LotSize area in 2015(implies-- some renovations)|\n"]},{"cell_type":"markdown","metadata":{},"source":["You will require the following libraries:\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import seaborn as sns\n","from sklearn.pipeline import Pipeline\n","from sklearn.preprocessing import StandardScaler,PolynomialFeatures\n","from sklearn.linear_model import LinearRegression\n","%matplotlib inline"]},{"cell_type":"markdown","metadata":{},"source":["# Module 1: Importing Data Sets\n"]},{"cell_type":"markdown","metadata":{},"source":["Load the csv:\n"]},{"cell_type":"code","execution_count":2,"metadata":{"jupyter":{"outputs_hidden":false}},"outputs":[],"source":["file_name='https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/kc_house_data_NaN.csv'\n","df=pd.read_csv(file_name)"]},{"cell_type":"markdown","metadata":{},"source":["We use the method <code>head</code> to display the first 5 columns of the dataframe.\n"]},{"cell_type":"code","execution_count":3,"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>Unnamed: 0</th>\n","      <th>id</th>\n","      <th>date</th>\n","      <th>price</th>\n","      <th>bedrooms</th>\n","      <th>bathrooms</th>\n","      <th>sqft_living</th>\n","      <th>sqft_lot</th>\n","      <th>floors</th>\n","      <th>waterfront</th>\n","      <th>...</th>\n","      <th>grade</th>\n","      <th>sqft_above</th>\n","      <th>sqft_basement</th>\n","      <th>yr_built</th>\n","      <th>yr_renovated</th>\n","      <th>zipcode</th>\n","      <th>lat</th>\n","      <th>long</th>\n","      <th>sqft_living15</th>\n","      <th>sqft_lot15</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>7129300520</td>\n","      <td>20141013T000000</td>\n","      <td>221900.0</td>\n","      <td>3.0</td>\n","      <td>1.00</td>\n","      <td>1180</td>\n","      <td>5650</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>7</td>\n","      <td>1180</td>\n","      <td>0</td>\n","      <td>1955</td>\n","      <td>0</td>\n","      <td>98178</td>\n","      <td>47.5112</td>\n","      <td>-122.257</td>\n","      <td>1340</td>\n","      <td>5650</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>6414100192</td>\n","      <td>20141209T000000</td>\n","      <td>538000.0</td>\n","      <td>3.0</td>\n","      <td>2.25</td>\n","      <td>2570</td>\n","      <td>7242</td>\n","      <td>2.0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>7</td>\n","      <td>2170</td>\n","      <td>400</td>\n","      <td>1951</td>\n","      <td>1991</td>\n","      <td>98125</td>\n","      <td>47.7210</td>\n","      <td>-122.319</td>\n","      <td>1690</td>\n","      <td>7639</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>5631500400</td>\n","      <td>20150225T000000</td>\n","      <td>180000.0</td>\n","      <td>2.0</td>\n","      <td>1.00</td>\n","      <td>770</td>\n","      <td>10000</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>6</td>\n","      <td>770</td>\n","      <td>0</td>\n","      <td>1933</td>\n","      <td>0</td>\n","      <td>98028</td>\n","      <td>47.7379</td>\n","      <td>-122.233</td>\n","      <td>2720</td>\n","      <td>8062</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>2487200875</td>\n","      <td>20141209T000000</td>\n","      <td>604000.0</td>\n","      <td>4.0</td>\n","      <td>3.00</td>\n","      <td>1960</td>\n","      <td>5000</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>7</td>\n","      <td>1050</td>\n","      <td>910</td>\n","      <td>1965</td>\n","      <td>0</td>\n","      <td>98136</td>\n","      <td>47.5208</td>\n","      <td>-122.393</td>\n","      <td>1360</td>\n","      <td>5000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>1954400510</td>\n","      <td>20150218T000000</td>\n","      <td>510000.0</td>\n","      <td>3.0</td>\n","      <td>2.00</td>\n","      <td>1680</td>\n","      <td>8080</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>8</td>\n","      <td>1680</td>\n","      <td>0</td>\n","      <td>1987</td>\n","      <td>0</td>\n","      <td>98074</td>\n","      <td>47.6168</td>\n","      <td>-122.045</td>\n","      <td>1800</td>\n","      <td>7503</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 22 columns</p>\n","</div>"],"text/plain":["   Unnamed: 0          id             date     price  bedrooms  bathrooms  \\\n","0           0  7129300520  20141013T000000  221900.0       3.0       1.00   \n","1           1  6414100192  20141209T000000  538000.0       3.0       2.25   \n","2           2  5631500400  20150225T000000  180000.0       2.0       1.00   \n","3           3  2487200875  20141209T000000  604000.0       4.0       3.00   \n","4           4  1954400510  20150218T000000  510000.0       3.0       2.00   \n","\n","   sqft_living  sqft_lot  floors  waterfront  ...  grade  sqft_above  \\\n","0         1180      5650     1.0           0  ...      7        1180   \n","1         2570      7242     2.0           0  ...      7        2170   \n","2          770     10000     1.0           0  ...      6         770   \n","3         1960      5000     1.0           0  ...      7        1050   \n","4         1680      8080     1.0           0  ...      8        1680   \n","\n","   sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n","0              0      1955             0    98178  47.5112 -122.257   \n","1            400      1951          1991    98125  47.7210 -122.319   \n","2              0      1933             0    98028  47.7379 -122.233   \n","3            910      1965             0    98136  47.5208 -122.393   \n","4              0      1987             0    98074  47.6168 -122.045   \n","\n","   sqft_living15  sqft_lot15  \n","0           1340        5650  \n","1           1690        7639  \n","2           2720        8062  \n","3           1360        5000  \n","4           1800        7503  \n","\n","[5 rows x 22 columns]"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"markdown","metadata":{},"source":["### Question 1\n","\n","Display the data types of each column using the function dtypes, then take a screenshot and submit it, include your code in the image.\n"]},{"cell_type":"code","execution_count":4,"metadata":{"jupyter":{"outputs_hidden":false}},"outputs":[{"data":{"text/plain":["Unnamed: 0         int64\n","id                 int64\n","date              object\n","price            float64\n","bedrooms         float64\n","bathrooms        float64\n","sqft_living        int64\n","sqft_lot           int64\n","floors           float64\n","waterfront         int64\n","view               int64\n","condition          int64\n","grade              int64\n","sqft_above         int64\n","sqft_basement      int64\n","yr_built           int64\n","yr_renovated       int64\n","zipcode            int64\n","lat              float64\n","long             float64\n","sqft_living15      int64\n","sqft_lot15         int64\n","dtype: object"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["df.dtypes"]},{"cell_type":"markdown","metadata":{},"source":["We use the method describe to obtain a statistical summary of the dataframe.\n"]},{"cell_type":"code","execution_count":5,"metadata":{"jupyter":{"outputs_hidden":false}},"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>Unnamed: 0</th>\n","      <th>id</th>\n","      <th>price</th>\n","      <th>bedrooms</th>\n","      <th>bathrooms</th>\n","      <th>sqft_living</th>\n","      <th>sqft_lot</th>\n","      <th>floors</th>\n","      <th>waterfront</th>\n","      <th>view</th>\n","      <th>...</th>\n","      <th>grade</th>\n","      <th>sqft_above</th>\n","      <th>sqft_basement</th>\n","      <th>yr_built</th>\n","      <th>yr_renovated</th>\n","      <th>zipcode</th>\n","      <th>lat</th>\n","      <th>long</th>\n","      <th>sqft_living15</th>\n","      <th>sqft_lot15</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>21613.00000</td>\n","      <td>2.161300e+04</td>\n","      <td>2.161300e+04</td>\n","      <td>21600.000000</td>\n","      <td>21603.000000</td>\n","      <td>21613.000000</td>\n","      <td>2.161300e+04</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>...</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>10806.00000</td>\n","      <td>4.580302e+09</td>\n","      <td>5.400881e+05</td>\n","      <td>3.372870</td>\n","      <td>2.115736</td>\n","      <td>2079.899736</td>\n","      <td>1.510697e+04</td>\n","      <td>1.494309</td>\n","      <td>0.007542</td>\n","      <td>0.234303</td>\n","      <td>...</td>\n","      <td>7.656873</td>\n","      <td>1788.390691</td>\n","      <td>291.509045</td>\n","      <td>1971.005136</td>\n","      <td>84.402258</td>\n","      <td>98077.939805</td>\n","      <td>47.560053</td>\n","      <td>-122.213896</td>\n","      <td>1986.552492</td>\n","      <td>12768.455652</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>6239.28002</td>\n","      <td>2.876566e+09</td>\n","      <td>3.671272e+05</td>\n","      <td>0.926657</td>\n","      <td>0.768996</td>\n","      <td>918.440897</td>\n","      <td>4.142051e+04</td>\n","      <td>0.539989</td>\n","      <td>0.086517</td>\n","      <td>0.766318</td>\n","      <td>...</td>\n","      <td>1.175459</td>\n","      <td>828.090978</td>\n","      <td>442.575043</td>\n","      <td>29.373411</td>\n","      <td>401.679240</td>\n","      <td>53.505026</td>\n","      <td>0.138564</td>\n","      <td>0.140828</td>\n","      <td>685.391304</td>\n","      <td>27304.179631</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.00000</td>\n","      <td>1.000102e+06</td>\n","      <td>7.500000e+04</td>\n","      <td>1.000000</td>\n","      <td>0.500000</td>\n","      <td>290.000000</td>\n","      <td>5.200000e+02</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>...</td>\n","      <td>1.000000</td>\n","      <td>290.000000</td>\n","      <td>0.000000</td>\n","      <td>1900.000000</td>\n","      <td>0.000000</td>\n","      <td>98001.000000</td>\n","      <td>47.155900</td>\n","      <td>-122.519000</td>\n","      <td>399.000000</td>\n","      <td>651.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>5403.00000</td>\n","      <td>2.123049e+09</td>\n","      <td>3.219500e+05</td>\n","      <td>3.000000</td>\n","      <td>1.750000</td>\n","      <td>1427.000000</td>\n","      <td>5.040000e+03</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>...</td>\n","      <td>7.000000</td>\n","      <td>1190.000000</td>\n","      <td>0.000000</td>\n","      <td>1951.000000</td>\n","      <td>0.000000</td>\n","      <td>98033.000000</td>\n","      <td>47.471000</td>\n","      <td>-122.328000</td>\n","      <td>1490.000000</td>\n","      <td>5100.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>10806.00000</td>\n","      <td>3.904930e+09</td>\n","      <td>4.500000e+05</td>\n","      <td>3.000000</td>\n","      <td>2.250000</td>\n","      <td>1910.000000</td>\n","      <td>7.618000e+03</td>\n","      <td>1.500000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>...</td>\n","      <td>7.000000</td>\n","      <td>1560.000000</td>\n","      <td>0.000000</td>\n","      <td>1975.000000</td>\n","      <td>0.000000</td>\n","      <td>98065.000000</td>\n","      <td>47.571800</td>\n","      <td>-122.230000</td>\n","      <td>1840.000000</td>\n","      <td>7620.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>16209.00000</td>\n","      <td>7.308900e+09</td>\n","      <td>6.450000e+05</td>\n","      <td>4.000000</td>\n","      <td>2.500000</td>\n","      <td>2550.000000</td>\n","      <td>1.068800e+04</td>\n","      <td>2.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>...</td>\n","      <td>8.000000</td>\n","      <td>2210.000000</td>\n","      <td>560.000000</td>\n","      <td>1997.000000</td>\n","      <td>0.000000</td>\n","      <td>98118.000000</td>\n","      <td>47.678000</td>\n","      <td>-122.125000</td>\n","      <td>2360.000000</td>\n","      <td>10083.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>21612.00000</td>\n","      <td>9.900000e+09</td>\n","      <td>7.700000e+06</td>\n","      <td>33.000000</td>\n","      <td>8.000000</td>\n","      <td>13540.000000</td>\n","      <td>1.651359e+06</td>\n","      <td>3.500000</td>\n","      <td>1.000000</td>\n","      <td>4.000000</td>\n","      <td>...</td>\n","      <td>13.000000</td>\n","      <td>9410.000000</td>\n","      <td>4820.000000</td>\n","      <td>2015.000000</td>\n","      <td>2015.000000</td>\n","      <td>98199.000000</td>\n","      <td>47.777600</td>\n","      <td>-121.315000</td>\n","      <td>6210.000000</td>\n","      <td>871200.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>8 rows × 21 columns</p>\n","</div>"],"text/plain":["        Unnamed: 0            id         price      bedrooms     bathrooms  \\\n","count  21613.00000  2.161300e+04  2.161300e+04  21600.000000  21603.000000   \n","mean   10806.00000  4.580302e+09  5.400881e+05      3.372870      2.115736   \n","std     6239.28002  2.876566e+09  3.671272e+05      0.926657      0.768996   \n","min        0.00000  1.000102e+06  7.500000e+04      1.000000      0.500000   \n","25%     5403.00000  2.123049e+09  3.219500e+05      3.000000      1.750000   \n","50%    10806.00000  3.904930e+09  4.500000e+05      3.000000      2.250000   \n","75%    16209.00000  7.308900e+09  6.450000e+05      4.000000      2.500000   \n","max    21612.00000  9.900000e+09  7.700000e+06     33.000000      8.000000   \n","\n","        sqft_living      sqft_lot        floors    waterfront          view  \\\n","count  21613.000000  2.161300e+04  21613.000000  21613.000000  21613.000000   \n","mean    2079.899736  1.510697e+04      1.494309      0.007542      0.234303   \n","std      918.440897  4.142051e+04      0.539989      0.086517      0.766318   \n","min      290.000000  5.200000e+02      1.000000      0.000000      0.000000   \n","25%     1427.000000  5.040000e+03      1.000000      0.000000      0.000000   \n","50%     1910.000000  7.618000e+03      1.500000      0.000000      0.000000   \n","75%     2550.000000  1.068800e+04      2.000000      0.000000      0.000000   \n","max    13540.000000  1.651359e+06      3.500000      1.000000      4.000000   \n","\n","       ...         grade    sqft_above  sqft_basement      yr_built  \\\n","count  ...  21613.000000  21613.000000   21613.000000  21613.000000   \n","mean   ...      7.656873   1788.390691     291.509045   1971.005136   \n","std    ...      1.175459    828.090978     442.575043     29.373411   \n","min    ...      1.000000    290.000000       0.000000   1900.000000   \n","25%    ...      7.000000   1190.000000       0.000000   1951.000000   \n","50%    ...      7.000000   1560.000000       0.000000   1975.000000   \n","75%    ...      8.000000   2210.000000     560.000000   1997.000000   \n","max    ...     13.000000   9410.000000    4820.000000   2015.000000   \n","\n","       yr_renovated       zipcode           lat          long  sqft_living15  \\\n","count  21613.000000  21613.000000  21613.000000  21613.000000   21613.000000   \n","mean      84.402258  98077.939805     47.560053   -122.213896    1986.552492   \n","std      401.679240     53.505026      0.138564      0.140828     685.391304   \n","min        0.000000  98001.000000     47.155900   -122.519000     399.000000   \n","25%        0.000000  98033.000000     47.471000   -122.328000    1490.000000   \n","50%        0.000000  98065.000000     47.571800   -122.230000    1840.000000   \n","75%        0.000000  98118.000000     47.678000   -122.125000    2360.000000   \n","max     2015.000000  98199.000000     47.777600   -121.315000    6210.000000   \n","\n","          sqft_lot15  \n","count   21613.000000  \n","mean    12768.455652  \n","std     27304.179631  \n","min       651.000000  \n","25%      5100.000000  \n","50%      7620.000000  \n","75%     10083.000000  \n","max    871200.000000  \n","\n","[8 rows x 21 columns]"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["df.describe()"]},{"cell_type":"markdown","metadata":{},"source":["# Module 2: Data Wrangling\n"]},{"cell_type":"markdown","metadata":{},"source":["### Question 2\n","\n","Drop the columns <code>\"id\"</code>  and <code>\"Unnamed: 0\"</code> from axis 1 using the method <code>drop()</code>, then use the method <code>describe()</code> to obtain a statistical summary of the data. Take a screenshot and submit it, make sure the <code>inplace</code> parameter is set to <code>True</code>\n"]},{"cell_type":"code","execution_count":6,"metadata":{"jupyter":{"outputs_hidden":false}},"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>bedrooms</th>\n","      <th>bathrooms</th>\n","      <th>sqft_living</th>\n","      <th>sqft_lot</th>\n","      <th>floors</th>\n","      <th>waterfront</th>\n","      <th>view</th>\n","      <th>condition</th>\n","      <th>grade</th>\n","      <th>sqft_above</th>\n","      <th>sqft_basement</th>\n","      <th>yr_built</th>\n","      <th>yr_renovated</th>\n","      <th>zipcode</th>\n","      <th>lat</th>\n","      <th>long</th>\n","      <th>sqft_living15</th>\n","      <th>sqft_lot15</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>2.161300e+04</td>\n","      <td>21600.000000</td>\n","      <td>21603.000000</td>\n","      <td>21613.000000</td>\n","      <td>2.161300e+04</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>5.400881e+05</td>\n","      <td>3.372870</td>\n","      <td>2.115736</td>\n","      <td>2079.899736</td>\n","      <td>1.510697e+04</td>\n","      <td>1.494309</td>\n","      <td>0.007542</td>\n","      <td>0.234303</td>\n","      <td>3.409430</td>\n","      <td>7.656873</td>\n","      <td>1788.390691</td>\n","      <td>291.509045</td>\n","      <td>1971.005136</td>\n","      <td>84.402258</td>\n","      <td>98077.939805</td>\n","      <td>47.560053</td>\n","      <td>-122.213896</td>\n","      <td>1986.552492</td>\n","      <td>12768.455652</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>3.671272e+05</td>\n","      <td>0.926657</td>\n","      <td>0.768996</td>\n","      <td>918.440897</td>\n","      <td>4.142051e+04</td>\n","      <td>0.539989</td>\n","      <td>0.086517</td>\n","      <td>0.766318</td>\n","      <td>0.650743</td>\n","      <td>1.175459</td>\n","      <td>828.090978</td>\n","      <td>442.575043</td>\n","      <td>29.373411</td>\n","      <td>401.679240</td>\n","      <td>53.505026</td>\n","      <td>0.138564</td>\n","      <td>0.140828</td>\n","      <td>685.391304</td>\n","      <td>27304.179631</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>7.500000e+04</td>\n","      <td>1.000000</td>\n","      <td>0.500000</td>\n","      <td>290.000000</td>\n","      <td>5.200000e+02</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>290.000000</td>\n","      <td>0.000000</td>\n","      <td>1900.000000</td>\n","      <td>0.000000</td>\n","      <td>98001.000000</td>\n","      <td>47.155900</td>\n","      <td>-122.519000</td>\n","      <td>399.000000</td>\n","      <td>651.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>3.219500e+05</td>\n","      <td>3.000000</td>\n","      <td>1.750000</td>\n","      <td>1427.000000</td>\n","      <td>5.040000e+03</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>3.000000</td>\n","      <td>7.000000</td>\n","      <td>1190.000000</td>\n","      <td>0.000000</td>\n","      <td>1951.000000</td>\n","      <td>0.000000</td>\n","      <td>98033.000000</td>\n","      <td>47.471000</td>\n","      <td>-122.328000</td>\n","      <td>1490.000000</td>\n","      <td>5100.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>4.500000e+05</td>\n","      <td>3.000000</td>\n","      <td>2.250000</td>\n","      <td>1910.000000</td>\n","      <td>7.618000e+03</td>\n","      <td>1.500000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>3.000000</td>\n","      <td>7.000000</td>\n","      <td>1560.000000</td>\n","      <td>0.000000</td>\n","      <td>1975.000000</td>\n","      <td>0.000000</td>\n","      <td>98065.000000</td>\n","      <td>47.571800</td>\n","      <td>-122.230000</td>\n","      <td>1840.000000</td>\n","      <td>7620.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>6.450000e+05</td>\n","      <td>4.000000</td>\n","      <td>2.500000</td>\n","      <td>2550.000000</td>\n","      <td>1.068800e+04</td>\n","      <td>2.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>4.000000</td>\n","      <td>8.000000</td>\n","      <td>2210.000000</td>\n","      <td>560.000000</td>\n","      <td>1997.000000</td>\n","      <td>0.000000</td>\n","      <td>98118.000000</td>\n","      <td>47.678000</td>\n","      <td>-122.125000</td>\n","      <td>2360.000000</td>\n","      <td>10083.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>7.700000e+06</td>\n","      <td>33.000000</td>\n","      <td>8.000000</td>\n","      <td>13540.000000</td>\n","      <td>1.651359e+06</td>\n","      <td>3.500000</td>\n","      <td>1.000000</td>\n","      <td>4.000000</td>\n","      <td>5.000000</td>\n","      <td>13.000000</td>\n","      <td>9410.000000</td>\n","      <td>4820.000000</td>\n","      <td>2015.000000</td>\n","      <td>2015.000000</td>\n","      <td>98199.000000</td>\n","      <td>47.777600</td>\n","      <td>-121.315000</td>\n","      <td>6210.000000</td>\n","      <td>871200.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["              price      bedrooms     bathrooms   sqft_living      sqft_lot  \\\n","count  2.161300e+04  21600.000000  21603.000000  21613.000000  2.161300e+04   \n","mean   5.400881e+05      3.372870      2.115736   2079.899736  1.510697e+04   \n","std    3.671272e+05      0.926657      0.768996    918.440897  4.142051e+04   \n","min    7.500000e+04      1.000000      0.500000    290.000000  5.200000e+02   \n","25%    3.219500e+05      3.000000      1.750000   1427.000000  5.040000e+03   \n","50%    4.500000e+05      3.000000      2.250000   1910.000000  7.618000e+03   \n","75%    6.450000e+05      4.000000      2.500000   2550.000000  1.068800e+04   \n","max    7.700000e+06     33.000000      8.000000  13540.000000  1.651359e+06   \n","\n","             floors    waterfront          view     condition         grade  \\\n","count  21613.000000  21613.000000  21613.000000  21613.000000  21613.000000   \n","mean       1.494309      0.007542      0.234303      3.409430      7.656873   \n","std        0.539989      0.086517      0.766318      0.650743      1.175459   \n","min        1.000000      0.000000      0.000000      1.000000      1.000000   \n","25%        1.000000      0.000000      0.000000      3.000000      7.000000   \n","50%        1.500000      0.000000      0.000000      3.000000      7.000000   \n","75%        2.000000      0.000000      0.000000      4.000000      8.000000   \n","max        3.500000      1.000000      4.000000      5.000000     13.000000   \n","\n","         sqft_above  sqft_basement      yr_built  yr_renovated       zipcode  \\\n","count  21613.000000   21613.000000  21613.000000  21613.000000  21613.000000   \n","mean    1788.390691     291.509045   1971.005136     84.402258  98077.939805   \n","std      828.090978     442.575043     29.373411    401.679240     53.505026   \n","min      290.000000       0.000000   1900.000000      0.000000  98001.000000   \n","25%     1190.000000       0.000000   1951.000000      0.000000  98033.000000   \n","50%     1560.000000       0.000000   1975.000000      0.000000  98065.000000   \n","75%     2210.000000     560.000000   1997.000000      0.000000  98118.000000   \n","max     9410.000000    4820.000000   2015.000000   2015.000000  98199.000000   \n","\n","                lat          long  sqft_living15     sqft_lot15  \n","count  21613.000000  21613.000000   21613.000000   21613.000000  \n","mean      47.560053   -122.213896    1986.552492   12768.455652  \n","std        0.138564      0.140828     685.391304   27304.179631  \n","min       47.155900   -122.519000     399.000000     651.000000  \n","25%       47.471000   -122.328000    1490.000000    5100.000000  \n","50%       47.571800   -122.230000    1840.000000    7620.000000  \n","75%       47.678000   -122.125000    2360.000000   10083.000000  \n","max       47.777600   -121.315000    6210.000000  871200.000000  "]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["df.drop(['id', 'Unnamed: 0'], axis = 1, inplace = True)\n","df.describe()"]},{"cell_type":"markdown","metadata":{},"source":["We can see we have missing values for the columns <code> bedrooms</code>  and <code> bathrooms </code>\n"]},{"cell_type":"code","execution_count":7,"metadata":{"jupyter":{"outputs_hidden":false}},"outputs":[{"name":"stdout","output_type":"stream","text":["number of NaN values for the column bedrooms : 13\n","number of NaN values for the column bathrooms : 10\n"]}],"source":["print(\"number of NaN values for the column bedrooms :\", df['bedrooms'].isnull().sum())\n","print(\"number of NaN values for the column bathrooms :\", df['bathrooms'].isnull().sum())\n"]},{"cell_type":"markdown","metadata":{},"source":["We can replace the missing values of the column <code>'bedrooms'</code> with the mean of the column  <code>'bedrooms' </code> using the method <code>replace()</code>. Don't forget to set the <code>inplace</code> parameter to <code>True</code>\n"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[],"source":["mean=df['bedrooms'].mean()\n","df['bedrooms'].replace(np.nan,mean, inplace=True)"]},{"cell_type":"markdown","metadata":{},"source":["We also replace the missing values of the column <code>'bathrooms'</code> with the mean of the column  <code>'bathrooms' </code> using the method <code>replace()</code>. Don't forget to set the <code> inplace </code>  parameter top <code> True </code>\n"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["mean=df['bathrooms'].mean()\n","df['bathrooms'].replace(np.nan,mean, inplace=True)"]},{"cell_type":"code","execution_count":10,"metadata":{"jupyter":{"outputs_hidden":false}},"outputs":[{"name":"stdout","output_type":"stream","text":["number of NaN values for the column bedrooms : 0\n","number of NaN values for the column bathrooms : 0\n"]}],"source":["print(\"number of NaN values for the column bedrooms :\", df['bedrooms'].isnull().sum())\n","print(\"number of NaN values for the column bathrooms :\", df['bathrooms'].isnull().sum())"]},{"cell_type":"markdown","metadata":{},"source":["# Module 3: Exploratory Data Analysis\n"]},{"cell_type":"markdown","metadata":{},"source":["### Question 3\n","\n","Use the method <code>value_counts</code> to count the number of houses with unique floor values, use the method <code>.to_frame()</code> to convert it to a dataframe.\n"]},{"cell_type":"code","execution_count":11,"metadata":{"jupyter":{"outputs_hidden":false}},"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>floors</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1.0</th>\n","      <td>10680</td>\n","    </tr>\n","    <tr>\n","      <th>2.0</th>\n","      <td>8241</td>\n","    </tr>\n","    <tr>\n","      <th>1.5</th>\n","      <td>1910</td>\n","    </tr>\n","    <tr>\n","      <th>3.0</th>\n","      <td>613</td>\n","    </tr>\n","    <tr>\n","      <th>2.5</th>\n","      <td>161</td>\n","    </tr>\n","    <tr>\n","      <th>3.5</th>\n","      <td>8</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     floors\n","1.0   10680\n","2.0    8241\n","1.5    1910\n","3.0     613\n","2.5     161\n","3.5       8"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["df['floors'].value_counts().to_frame()"]},{"cell_type":"markdown","metadata":{},"source":["### Question 4\n","\n","Use the function <code>boxplot</code> in the seaborn library  to  determine whether houses with a waterfront view or without a waterfront view have more price outliers.\n"]},{"cell_type":"code","execution_count":12,"metadata":{"jupyter":{"outputs_hidden":false}},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7feb201d0a10>"]},"execution_count":12,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.boxplot(df['waterfront'], df['price'])"]},{"cell_type":"markdown","metadata":{},"source":["### Question 5\n","\n","Use the function <code>regplot</code>  in the seaborn library  to  determine if the feature <code>sqft_above</code> is negatively or positively correlated with price.\n"]},{"cell_type":"code","execution_count":13,"metadata":{"jupyter":{"outputs_hidden":false}},"outputs":[{"data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7feb1b964950>"]},"execution_count":13,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.regplot(df['sqft_above'], df['price'])"]},{"cell_type":"markdown","metadata":{},"source":["We can use the Pandas method <code>corr()</code>  to find the feature other than price that is most correlated with price.\n"]},{"cell_type":"code","execution_count":14,"metadata":{"jupyter":{"outputs_hidden":false}},"outputs":[{"data":{"text/plain":["zipcode         -0.053203\n","long             0.021626\n","condition        0.036362\n","yr_built         0.054012\n","sqft_lot15       0.082447\n","sqft_lot         0.089661\n","yr_renovated     0.126434\n","floors           0.256794\n","waterfront       0.266369\n","lat              0.307003\n","bedrooms         0.308797\n","sqft_basement    0.323816\n","view             0.397293\n","bathrooms        0.525738\n","sqft_living15    0.585379\n","sqft_above       0.605567\n","grade            0.667434\n","sqft_living      0.702035\n","price            1.000000\n","Name: price, dtype: float64"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["df.corr()['price'].sort_values()"]},{"cell_type":"markdown","metadata":{},"source":["# Module 4: Model Development\n"]},{"cell_type":"markdown","metadata":{},"source":["We can Fit a linear regression model using the  longitude feature <code>'long'</code> and  caculate the R^2.\n"]},{"cell_type":"code","execution_count":15,"metadata":{"jupyter":{"outputs_hidden":false}},"outputs":[{"data":{"text/plain":["0.00046769430149007363"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["X = df[['long']]\n","Y = df['price']\n","lm = LinearRegression()\n","lm.fit(X,Y)\n","lm.score(X, Y)"]},{"cell_type":"markdown","metadata":{},"source":["### Question  6\n","\n","Fit a linear regression model to predict the <code>'price'</code> using the feature <code>'sqft_living'</code> then calculate the R^2. Take a screenshot of your code and the value of the R^2.\n"]},{"cell_type":"code","execution_count":16,"metadata":{"jupyter":{"outputs_hidden":false}},"outputs":[{"data":{"text/plain":["0.4928532179037931"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["X1 = df[['sqft_living']]\n","lm.fit(X1,Y)\n","lm.score(X1, Y)"]},{"cell_type":"markdown","metadata":{},"source":["### Question 7\n","\n","Fit a linear regression model to predict the <code>'price'</code> using the list of features:\n"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[],"source":["features =[\"floors\", \"waterfront\",\"lat\" ,\"bedrooms\" ,\"sqft_basement\" ,\"view\" ,\"bathrooms\",\"sqft_living15\",\"sqft_above\",\"grade\",\"sqft_living\"]     "]},{"cell_type":"markdown","metadata":{},"source":["Then calculate the R^2. Take a screenshot of your code.\n"]},{"cell_type":"code","execution_count":18,"metadata":{"jupyter":{"outputs_hidden":false}},"outputs":[{"data":{"text/plain":["0.657679183672129"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["lm.fit(df[features], Y)\n","lm.score(df[features], Y)"]},{"cell_type":"markdown","metadata":{},"source":["### This will help with Question 8\n","\n","Create a list of tuples, the first element in the tuple contains the name of the estimator:\n","\n","<code>'scale'</code>\n","\n","<code>'polynomial'</code>\n","\n","<code>'model'</code>\n","\n","The second element in the tuple  contains the model constructor\n","\n","<code>StandardScaler()</code>\n","\n","<code>PolynomialFeatures(include_bias=False)</code>\n","\n","<code>LinearRegression()</code>\n"]},{"cell_type":"code","execution_count":19,"metadata":{},"outputs":[],"source":["Input=[('scale',StandardScaler()),('polynomial', PolynomialFeatures(include_bias=False)),('model',LinearRegression())]"]},{"cell_type":"markdown","metadata":{},"source":["### Question 8\n","\n","Use the list to create a pipeline object to predict the 'price', fit the object using the features in the list <code>features</code>, and calculate the R^2.\n"]},{"cell_type":"code","execution_count":20,"metadata":{"jupyter":{"outputs_hidden":false}},"outputs":[{"data":{"text/plain":["0.7513408553309376"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["Pipe = Pipeline(Input)\n","Pipe.fit(df[features],Y)\n","Pipe.score(df[features],Y)"]},{"cell_type":"markdown","metadata":{},"source":["# Module 5: Model Evaluation and Refinement\n"]},{"cell_type":"markdown","metadata":{},"source":["Import the necessary modules:\n"]},{"cell_type":"code","execution_count":21,"metadata":{"jupyter":{"outputs_hidden":false}},"outputs":[{"name":"stdout","output_type":"stream","text":["done\n"]}],"source":["from sklearn.model_selection import cross_val_score\n","from sklearn.model_selection import train_test_split\n","print(\"done\")"]},{"cell_type":"markdown","metadata":{},"source":["We will split the data into training and testing sets:\n"]},{"cell_type":"code","execution_count":22,"metadata":{"jupyter":{"outputs_hidden":false}},"outputs":[{"name":"stdout","output_type":"stream","text":["number of test samples: 3242\n","number of training samples: 18371\n"]}],"source":["features =[\"floors\", \"waterfront\",\"lat\" ,\"bedrooms\" ,\"sqft_basement\" ,\"view\" ,\"bathrooms\",\"sqft_living15\",\"sqft_above\",\"grade\",\"sqft_living\"]    \n","X = df[features]\n","Y = df['price']\n","\n","x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.15, random_state=1)\n","\n","\n","print(\"number of test samples:\", x_test.shape[0])\n","print(\"number of training samples:\",x_train.shape[0])"]},{"cell_type":"markdown","metadata":{},"source":["### Question 9\n","\n","Create and fit a Ridge regression object using the training data, set the regularization parameter to 0.1, and calculate the R^2 using the test data.\n"]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[],"source":["from sklearn.linear_model import Ridge"]},{"cell_type":"code","execution_count":27,"metadata":{"jupyter":{"outputs_hidden":false}},"outputs":[{"data":{"text/plain":["0.6478759163939122"]},"execution_count":27,"metadata":{},"output_type":"execute_result"}],"source":["RM = Ridge(alpha = 0.1)\n","RM.fit(x_train,y_train)\n","RM.score(x_test,y_test)"]},{"cell_type":"markdown","metadata":{},"source":["### Question 10\n","\n","Perform a second order polynomial transform on both the training data and testing data. Create and fit a Ridge regression object using the training data, set the regularisation parameter to 0.1, and calculate the R^2 utilising the test data provided. Take a screenshot of your code and the R^2.\n"]},{"cell_type":"code","execution_count":29,"metadata":{"jupyter":{"outputs_hidden":false}},"outputs":[{"data":{"text/plain":["0.7002744279896707"]},"execution_count":29,"metadata":{},"output_type":"execute_result"}],"source":["pr = PolynomialFeatures(degree = 2)\n","x_train_pr = pr.fit_transform(x_train[features])\n","x_test_pr = pr.fit_transform(x_test[features])\n","\n","RM = Ridge(alpha = 0.1)\n","RM.fit(x_train_pr, y_train)\n","RM.score(x_test_pr, y_test)\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3.7","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.10"},"widgets":{"state":{},"version":"1.1.2"}},"nbformat":4,"nbformat_minor":4}