{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-05-23T19:58:28.967331Z","iopub.execute_input":"2023-05-23T19:58:28.967778Z","iopub.status.idle":"2023-05-23T19:58:28.985038Z","shell.execute_reply.started":"2023-05-23T19:58:28.967744Z","shell.execute_reply":"2023-05-23T19:58:28.983075Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"/kaggle/input/housedata/output.csv\n/kaggle/input/housedata/data.csv\n/kaggle/input/housedata/data.dat\n","output_type":"stream"}]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:28.987809Z","iopub.execute_input":"2023-05-23T19:58:28.989104Z","iopub.status.idle":"2023-05-23T19:58:29.406592Z","shell.execute_reply.started":"2023-05-23T19:58:28.989049Z","shell.execute_reply":"2023-05-23T19:58:29.405208Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"houseprice=pd.read_csv('/kaggle/input/housedata/data.csv')","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.408396Z","iopub.execute_input":"2023-05-23T19:58:29.408791Z","iopub.status.idle":"2023-05-23T19:58:29.464198Z","shell.execute_reply.started":"2023-05-23T19:58:29.408755Z","shell.execute_reply":"2023-05-23T19:58:29.463007Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"houseprice","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.466593Z","iopub.execute_input":"2023-05-23T19:58:29.467760Z","iopub.status.idle":"2023-05-23T19:58:29.532655Z","shell.execute_reply.started":"2023-05-23T19:58:29.467720Z","shell.execute_reply":"2023-05-23T19:58:29.531096Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":" date price bedrooms bathrooms sqft_living \\\n0 2014-05-02 00:00:00 3.130000e+05 3.0 1.50 1340 \n1 2014-05-02 00:00:00 2.384000e+06 5.0 2.50 3650 \n2 2014-05-02 00:00:00 3.420000e+05 3.0 2.00 1930 \n3 2014-05-02 00:00:00 4.200000e+05 3.0 2.25 2000 \n4 2014-05-02 00:00:00 5.500000e+05 4.0 2.50 1940 \n... ... ... ... ... ... \n4595 2014-07-09 00:00:00 3.081667e+05 3.0 1.75 1510 \n4596 2014-07-09 00:00:00 5.343333e+05 3.0 2.50 1460 \n4597 2014-07-09 00:00:00 4.169042e+05 3.0 2.50 3010 \n4598 2014-07-10 00:00:00 2.034000e+05 4.0 2.00 2090 \n4599 2014-07-10 00:00:00 2.206000e+05 3.0 2.50 1490 \n\n sqft_lot floors waterfront view condition sqft_above \\\n0 7912 1.5 0 0 3 1340 \n1 9050 2.0 0 4 5 3370 \n2 11947 1.0 0 0 4 1930 \n3 8030 1.0 0 0 4 1000 \n4 10500 1.0 0 0 4 1140 \n... ... ... ... ... ... ... \n4595 6360 1.0 0 0 4 1510 \n4596 7573 2.0 0 0 3 1460 \n4597 7014 2.0 0 0 3 3010 \n4598 6630 1.0 0 0 3 1070 \n4599 8102 2.0 0 0 4 1490 \n\n sqft_basement yr_built yr_renovated street \\\n0 0 1955 2005 18810 Densmore Ave N \n1 280 1921 0 709 W Blaine St \n2 0 1966 0 26206-26214 143rd Ave SE \n3 1000 1963 0 857 170th Pl NE \n4 800 1976 1992 9105 170th Ave NE \n... ... ... ... ... \n4595 0 1954 1979 501 N 143rd St \n4596 0 1983 2009 14855 SE 10th Pl \n4597 0 2009 0 759 Ilwaco Pl NE \n4598 1020 1974 0 5148 S Creston St \n4599 0 1990 0 18717 SE 258th St \n\n city statezip country \n0 Shoreline WA 98133 USA \n1 Seattle WA 98119 USA \n2 Kent WA 98042 USA \n3 Bellevue WA 98008 USA \n4 Redmond WA 98052 USA \n... ... ... ... \n4595 Seattle WA 98133 USA \n4596 Bellevue WA 98007 USA \n4597 Renton WA 98059 USA \n4598 Seattle WA 98178 USA \n4599 Covington WA 98042 USA \n\n[4600 rows x 18 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
datepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditionsqft_abovesqft_basementyr_builtyr_renovatedstreetcitystatezipcountry
02014-05-02 00:00:003.130000e+053.01.50134079121.5003134001955200518810 Densmore Ave NShorelineWA 98133USA
12014-05-02 00:00:002.384000e+065.02.50365090502.0045337028019210709 W Blaine StSeattleWA 98119USA
22014-05-02 00:00:003.420000e+053.02.001930119471.0004193001966026206-26214 143rd Ave SEKentWA 98042USA
32014-05-02 00:00:004.200000e+053.02.25200080301.00041000100019630857 170th Pl NEBellevueWA 98008USA
42014-05-02 00:00:005.500000e+054.02.501940105001.00041140800197619929105 170th Ave NERedmondWA 98052USA
.........................................................
45952014-07-09 00:00:003.081667e+053.01.75151063601.00041510019541979501 N 143rd StSeattleWA 98133USA
45962014-07-09 00:00:005.343333e+053.02.50146075732.0003146001983200914855 SE 10th PlBellevueWA 98007USA
45972014-07-09 00:00:004.169042e+053.02.50301070142.00033010020090759 Ilwaco Pl NERentonWA 98059USA
45982014-07-10 00:00:002.034000e+054.02.00209066301.000310701020197405148 S Creston StSeattleWA 98178USA
45992014-07-10 00:00:002.206000e+053.02.50149081022.0004149001990018717 SE 258th StCovingtonWA 98042USA
\n

4600 rows × 18 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"pd.set_option('display.float_format', lambda x: '%.3f' % x)\n","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.533953Z","iopub.execute_input":"2023-05-23T19:58:29.534349Z","iopub.status.idle":"2023-05-23T19:58:29.541548Z","shell.execute_reply.started":"2023-05-23T19:58:29.534317Z","shell.execute_reply":"2023-05-23T19:58:29.539661Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"houseprice","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.543084Z","iopub.execute_input":"2023-05-23T19:58:29.543539Z","iopub.status.idle":"2023-05-23T19:58:29.576677Z","shell.execute_reply.started":"2023-05-23T19:58:29.543510Z","shell.execute_reply":"2023-05-23T19:58:29.575126Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":" date price bedrooms bathrooms sqft_living \\\n0 2014-05-02 00:00:00 313000.000 3.000 1.500 1340 \n1 2014-05-02 00:00:00 2384000.000 5.000 2.500 3650 \n2 2014-05-02 00:00:00 342000.000 3.000 2.000 1930 \n3 2014-05-02 00:00:00 420000.000 3.000 2.250 2000 \n4 2014-05-02 00:00:00 550000.000 4.000 2.500 1940 \n... ... ... ... ... ... \n4595 2014-07-09 00:00:00 308166.667 3.000 1.750 1510 \n4596 2014-07-09 00:00:00 534333.333 3.000 2.500 1460 \n4597 2014-07-09 00:00:00 416904.167 3.000 2.500 3010 \n4598 2014-07-10 00:00:00 203400.000 4.000 2.000 2090 \n4599 2014-07-10 00:00:00 220600.000 3.000 2.500 1490 \n\n sqft_lot floors waterfront view condition sqft_above \\\n0 7912 1.500 0 0 3 1340 \n1 9050 2.000 0 4 5 3370 \n2 11947 1.000 0 0 4 1930 \n3 8030 1.000 0 0 4 1000 \n4 10500 1.000 0 0 4 1140 \n... ... ... ... ... ... ... \n4595 6360 1.000 0 0 4 1510 \n4596 7573 2.000 0 0 3 1460 \n4597 7014 2.000 0 0 3 3010 \n4598 6630 1.000 0 0 3 1070 \n4599 8102 2.000 0 0 4 1490 \n\n sqft_basement yr_built yr_renovated street \\\n0 0 1955 2005 18810 Densmore Ave N \n1 280 1921 0 709 W Blaine St \n2 0 1966 0 26206-26214 143rd Ave SE \n3 1000 1963 0 857 170th Pl NE \n4 800 1976 1992 9105 170th Ave NE \n... ... ... ... ... \n4595 0 1954 1979 501 N 143rd St \n4596 0 1983 2009 14855 SE 10th Pl \n4597 0 2009 0 759 Ilwaco Pl NE \n4598 1020 1974 0 5148 S Creston St \n4599 0 1990 0 18717 SE 258th St \n\n city statezip country \n0 Shoreline WA 98133 USA \n1 Seattle WA 98119 USA \n2 Kent WA 98042 USA \n3 Bellevue WA 98008 USA \n4 Redmond WA 98052 USA \n... ... ... ... \n4595 Seattle WA 98133 USA \n4596 Bellevue WA 98007 USA \n4597 Renton WA 98059 USA \n4598 Seattle WA 98178 USA \n4599 Covington WA 98042 USA \n\n[4600 rows x 18 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
datepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditionsqft_abovesqft_basementyr_builtyr_renovatedstreetcitystatezipcountry
02014-05-02 00:00:00313000.0003.0001.500134079121.500003134001955200518810 Densmore Ave NShorelineWA 98133USA
12014-05-02 00:00:002384000.0005.0002.500365090502.000045337028019210709 W Blaine StSeattleWA 98119USA
22014-05-02 00:00:00342000.0003.0002.0001930119471.000004193001966026206-26214 143rd Ave SEKentWA 98042USA
32014-05-02 00:00:00420000.0003.0002.250200080301.0000041000100019630857 170th Pl NEBellevueWA 98008USA
42014-05-02 00:00:00550000.0004.0002.5001940105001.0000041140800197619929105 170th Ave NERedmondWA 98052USA
.........................................................
45952014-07-09 00:00:00308166.6673.0001.750151063601.0000041510019541979501 N 143rd StSeattleWA 98133USA
45962014-07-09 00:00:00534333.3333.0002.500146075732.000003146001983200914855 SE 10th PlBellevueWA 98007USA
45972014-07-09 00:00:00416904.1673.0002.500301070142.0000033010020090759 Ilwaco Pl NERentonWA 98059USA
45982014-07-10 00:00:00203400.0004.0002.000209066301.00000310701020197405148 S Creston StSeattleWA 98178USA
45992014-07-10 00:00:00220600.0003.0002.500149081022.000004149001990018717 SE 258th StCovingtonWA 98042USA
\n

4600 rows × 18 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"houseprice.describe()","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.578282Z","iopub.execute_input":"2023-05-23T19:58:29.578762Z","iopub.status.idle":"2023-05-23T19:58:29.643935Z","shell.execute_reply.started":"2023-05-23T19:58:29.578722Z","shell.execute_reply":"2023-05-23T19:58:29.642701Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":" price bedrooms bathrooms sqft_living sqft_lot floors \\\ncount 4600.000 4600.000 4600.000 4600.000 4600.000 4600.000 \nmean 551962.988 3.401 2.161 2139.347 14852.516 1.512 \nstd 563834.703 0.909 0.784 963.207 35884.436 0.538 \nmin 0.000 0.000 0.000 370.000 638.000 1.000 \n25% 322875.000 3.000 1.750 1460.000 5000.750 1.000 \n50% 460943.462 3.000 2.250 1980.000 7683.000 1.500 \n75% 654962.500 4.000 2.500 2620.000 11001.250 2.000 \nmax 26590000.000 9.000 8.000 13540.000 1074218.000 3.500 \n\n waterfront view condition sqft_above sqft_basement yr_built \\\ncount 4600.000 4600.000 4600.000 4600.000 4600.000 4600.000 \nmean 0.007 0.241 3.452 1827.265 312.082 1970.786 \nstd 0.084 0.778 0.677 862.169 464.137 29.732 \nmin 0.000 0.000 1.000 370.000 0.000 1900.000 \n25% 0.000 0.000 3.000 1190.000 0.000 1951.000 \n50% 0.000 0.000 3.000 1590.000 0.000 1976.000 \n75% 0.000 0.000 4.000 2300.000 610.000 1997.000 \nmax 1.000 4.000 5.000 9410.000 4820.000 2014.000 \n\n yr_renovated \ncount 4600.000 \nmean 808.608 \nstd 979.415 \nmin 0.000 \n25% 0.000 \n50% 0.000 \n75% 1999.000 \nmax 2014.000 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
pricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditionsqft_abovesqft_basementyr_builtyr_renovated
count4600.0004600.0004600.0004600.0004600.0004600.0004600.0004600.0004600.0004600.0004600.0004600.0004600.000
mean551962.9883.4012.1612139.34714852.5161.5120.0070.2413.4521827.265312.0821970.786808.608
std563834.7030.9090.784963.20735884.4360.5380.0840.7780.677862.169464.13729.732979.415
min0.0000.0000.000370.000638.0001.0000.0000.0001.000370.0000.0001900.0000.000
25%322875.0003.0001.7501460.0005000.7501.0000.0000.0003.0001190.0000.0001951.0000.000
50%460943.4623.0002.2501980.0007683.0001.5000.0000.0003.0001590.0000.0001976.0000.000
75%654962.5004.0002.5002620.00011001.2502.0000.0000.0004.0002300.000610.0001997.0001999.000
max26590000.0009.0008.00013540.0001074218.0003.5001.0004.0005.0009410.0004820.0002014.0002014.000
\n
"},"metadata":{}}]},{"cell_type":"code","source":"houseprice.info()# feature X's bedrooms, sqft_living,sqft_lot,floors,view,yr_bulit, #output=price\n","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.645885Z","iopub.execute_input":"2023-05-23T19:58:29.646269Z","iopub.status.idle":"2023-05-23T19:58:29.684303Z","shell.execute_reply.started":"2023-05-23T19:58:29.646240Z","shell.execute_reply":"2023-05-23T19:58:29.682974Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"\nRangeIndex: 4600 entries, 0 to 4599\nData columns (total 18 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 date 4600 non-null object \n 1 price 4600 non-null float64\n 2 bedrooms 4600 non-null float64\n 3 bathrooms 4600 non-null float64\n 4 sqft_living 4600 non-null int64 \n 5 sqft_lot 4600 non-null int64 \n 6 floors 4600 non-null float64\n 7 waterfront 4600 non-null int64 \n 8 view 4600 non-null int64 \n 9 condition 4600 non-null int64 \n 10 sqft_above 4600 non-null int64 \n 11 sqft_basement 4600 non-null int64 \n 12 yr_built 4600 non-null int64 \n 13 yr_renovated 4600 non-null int64 \n 14 street 4600 non-null object \n 15 city 4600 non-null object \n 16 statezip 4600 non-null object \n 17 country 4600 non-null object \ndtypes: float64(4), int64(9), object(5)\nmemory usage: 647.0+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"houseprice.drop(['date','city','street','statezip','condition'], axis=1)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.690603Z","iopub.execute_input":"2023-05-23T19:58:29.691907Z","iopub.status.idle":"2023-05-23T19:58:29.716413Z","shell.execute_reply.started":"2023-05-23T19:58:29.691848Z","shell.execute_reply":"2023-05-23T19:58:29.714825Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":" price bedrooms bathrooms sqft_living sqft_lot floors \\\n0 313000.000 3.000 1.500 1340 7912 1.500 \n1 2384000.000 5.000 2.500 3650 9050 2.000 \n2 342000.000 3.000 2.000 1930 11947 1.000 \n3 420000.000 3.000 2.250 2000 8030 1.000 \n4 550000.000 4.000 2.500 1940 10500 1.000 \n... ... ... ... ... ... ... \n4595 308166.667 3.000 1.750 1510 6360 1.000 \n4596 534333.333 3.000 2.500 1460 7573 2.000 \n4597 416904.167 3.000 2.500 3010 7014 2.000 \n4598 203400.000 4.000 2.000 2090 6630 1.000 \n4599 220600.000 3.000 2.500 1490 8102 2.000 \n\n waterfront view sqft_above sqft_basement yr_built yr_renovated \\\n0 0 0 1340 0 1955 2005 \n1 0 4 3370 280 1921 0 \n2 0 0 1930 0 1966 0 \n3 0 0 1000 1000 1963 0 \n4 0 0 1140 800 1976 1992 \n... ... ... ... ... ... ... \n4595 0 0 1510 0 1954 1979 \n4596 0 0 1460 0 1983 2009 \n4597 0 0 3010 0 2009 0 \n4598 0 0 1070 1020 1974 0 \n4599 0 0 1490 0 1990 0 \n\n country \n0 USA \n1 USA \n2 USA \n3 USA \n4 USA \n... ... \n4595 USA \n4596 USA \n4597 USA \n4598 USA \n4599 USA \n\n[4600 rows x 13 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
pricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewsqft_abovesqft_basementyr_builtyr_renovatedcountry
0313000.0003.0001.500134079121.500001340019552005USA
12384000.0005.0002.500365090502.00004337028019210USA
2342000.0003.0002.0001930119471.000001930019660USA
3420000.0003.0002.250200080301.000001000100019630USA
4550000.0004.0002.5001940105001.00000114080019761992USA
..........................................
4595308166.6673.0001.750151063601.000001510019541979USA
4596534333.3333.0002.500146075732.000001460019832009USA
4597416904.1673.0002.500301070142.000003010020090USA
4598203400.0004.0002.000209066301.000001070102019740USA
4599220600.0003.0002.500149081022.000001490019900USA
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4600 rows × 13 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"X=houseprice.drop(['price','date','city','street','statezip','condition'],axis=1)\nY=houseprice['price']","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.718292Z","iopub.execute_input":"2023-05-23T19:58:29.718801Z","iopub.status.idle":"2023-05-23T19:58:29.734752Z","shell.execute_reply.started":"2023-05-23T19:58:29.718754Z","shell.execute_reply":"2023-05-23T19:58:29.733636Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.736430Z","iopub.execute_input":"2023-05-23T19:58:29.737367Z","iopub.status.idle":"2023-05-23T19:58:29.768553Z","shell.execute_reply.started":"2023-05-23T19:58:29.737327Z","shell.execute_reply":"2023-05-23T19:58:29.767142Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":" bedrooms bathrooms sqft_living sqft_lot floors waterfront view \\\n0 3.000 1.500 1340 7912 1.500 0 0 \n1 5.000 2.500 3650 9050 2.000 0 4 \n2 3.000 2.000 1930 11947 1.000 0 0 \n3 3.000 2.250 2000 8030 1.000 0 0 \n4 4.000 2.500 1940 10500 1.000 0 0 \n... ... ... ... ... ... ... ... \n4595 3.000 1.750 1510 6360 1.000 0 0 \n4596 3.000 2.500 1460 7573 2.000 0 0 \n4597 3.000 2.500 3010 7014 2.000 0 0 \n4598 4.000 2.000 2090 6630 1.000 0 0 \n4599 3.000 2.500 1490 8102 2.000 0 0 \n\n sqft_above sqft_basement yr_built yr_renovated country \n0 1340 0 1955 2005 USA \n1 3370 280 1921 0 USA \n2 1930 0 1966 0 USA \n3 1000 1000 1963 0 USA \n4 1140 800 1976 1992 USA \n... ... ... ... ... ... \n4595 1510 0 1954 1979 USA \n4596 1460 0 1983 2009 USA \n4597 3010 0 2009 0 USA \n4598 1070 1020 1974 0 USA \n4599 1490 0 1990 0 USA \n\n[4600 rows x 12 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
bedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewsqft_abovesqft_basementyr_builtyr_renovatedcountry
03.0001.500134079121.500001340019552005USA
15.0002.500365090502.00004337028019210USA
23.0002.0001930119471.000001930019660USA
33.0002.250200080301.000001000100019630USA
44.0002.5001940105001.00000114080019761992USA
.......................................
45953.0001.750151063601.000001510019541979USA
45963.0002.500146075732.000001460019832009USA
45973.0002.500301070142.000003010020090USA
45984.0002.000209066301.000001070102019740USA
45993.0002.500149081022.000001490019900USA
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4600 rows × 12 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"Y","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.770068Z","iopub.execute_input":"2023-05-23T19:58:29.770420Z","iopub.status.idle":"2023-05-23T19:58:29.787392Z","shell.execute_reply.started":"2023-05-23T19:58:29.770392Z","shell.execute_reply":"2023-05-23T19:58:29.786439Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"0 313000.000\n1 2384000.000\n2 342000.000\n3 420000.000\n4 550000.000\n ... \n4595 308166.667\n4596 534333.333\n4597 416904.167\n4598 203400.000\n4599 220600.000\nName: price, Length: 4600, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"X.shape\n","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.788761Z","iopub.execute_input":"2023-05-23T19:58:29.789531Z","iopub.status.idle":"2023-05-23T19:58:29.807075Z","shell.execute_reply.started":"2023-05-23T19:58:29.789493Z","shell.execute_reply":"2023-05-23T19:58:29.805718Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"(4600, 12)"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nX_train,X_test,Y_train,Y_test=train_test_split(X.values,Y.values,test_size=0.2)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.808843Z","iopub.execute_input":"2023-05-23T19:58:29.809363Z","iopub.status.idle":"2023-05-23T19:58:29.827151Z","shell.execute_reply.started":"2023-05-23T19:58:29.809318Z","shell.execute_reply":"2023-05-23T19:58:29.825801Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"X_train","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.828473Z","iopub.execute_input":"2023-05-23T19:58:29.828944Z","iopub.status.idle":"2023-05-23T19:58:29.843063Z","shell.execute_reply.started":"2023-05-23T19:58:29.828892Z","shell.execute_reply":"2023-05-23T19:58:29.842108Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"array([[4.0, 1.5, 1220, ..., 1965, 1993, 'USA'],\n [2.0, 2.5, 1050, ..., 2007, 0, 'USA'],\n [2.0, 1.75, 1590, ..., 1927, 2011, 'USA'],\n ...,\n [5.0, 3.25, 3410, ..., 1912, 1994, 'USA'],\n [4.0, 2.5, 1810, ..., 1994, 0, 'USA'],\n [3.0, 2.5, 2340, ..., 1995, 0, 'USA']], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"Y_train","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.844425Z","iopub.execute_input":"2023-05-23T19:58:29.844842Z","iopub.status.idle":"2023-05-23T19:58:29.863388Z","shell.execute_reply.started":"2023-05-23T19:58:29.844796Z","shell.execute_reply":"2023-05-23T19:58:29.861685Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"array([ 418000., 332888., 850000., ..., 2400000., 270000., 426090.])"},"metadata":{}}]},{"cell_type":"code","source":"X_test","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.864971Z","iopub.execute_input":"2023-05-23T19:58:29.865367Z","iopub.status.idle":"2023-05-23T19:58:29.882598Z","shell.execute_reply.started":"2023-05-23T19:58:29.865321Z","shell.execute_reply":"2023-05-23T19:58:29.881333Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"array([[4.0, 2.5, 1970, ..., 1987, 2000, 'USA'],\n [3.0, 2.5, 2490, ..., 2003, 0, 'USA'],\n [3.0, 2.75, 3890, ..., 1967, 2010, 'USA'],\n ...,\n [4.0, 1.5, 1920, ..., 1959, 1989, 'USA'],\n [3.0, 2.75, 3010, ..., 2011, 0, 'USA'],\n [4.0, 2.5, 3300, ..., 1984, 0, 'USA']], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"Y_test","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.884278Z","iopub.execute_input":"2023-05-23T19:58:29.884627Z","iopub.status.idle":"2023-05-23T19:58:29.911868Z","shell.execute_reply.started":"2023-05-23T19:58:29.884598Z","shell.execute_reply":"2023-05-23T19:58:29.910534Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"array([ 274900. , 705380. , 1080000. , 950000. ,\n 592500. , 880000. , 285000. , 739888. ,\n 223000. , 540000. , 366000. , 979000. ,\n 540000. , 739000. , 790000. , 1346400. ,\n 440000. , 310000. , 475000. , 360000. ,\n 1170000. , 415000. , 681500. , 219950. ,\n 670000. , 286000. , 475000. , 470000. ,\n 560000. , 246500. , 315275. , 760000. ,\n 300000. , 215000. , 355300. , 212500. ,\n 552000. , 279000. , 800000. , 375000. ,\n 650000. , 485000. , 1149000. , 471000. ,\n 242000. , 405000. , 475000. , 0. ,\n 477000. , 292600. , 418000. , 248000. ,\n 649000. , 625000. , 320000. , 537500. ,\n 335000. , 285500. , 812000. , 558000. ,\n 376000. , 546000. , 370000. , 435000. ,\n 1356925. , 503000. , 305000. , 762500. ,\n 410000. , 660000. , 795000. , 450000. ,\n 330000. , 430000. , 225000. , 0. ,\n 300000. , 242000. , 215000. , 660000. ,\n 430000. , 400000. , 670500. , 362000. ,\n 415000. , 435000. , 275000. , 355000. ,\n 439000. , 690000. , 294000. , 838000. ,\n 374000. , 1130000. , 309950. , 335500. ,\n 527000. , 810000. , 732000. , 750000. ,\n 440000. , 975000. , 544500. , 319950. ,\n 381000. , 363990. , 2400000. , 1710000. ,\n 1150000. , 700000. , 258000. , 476000. ,\n 285000. , 310000. , 450000. , 268000. ,\n 600000. , 315000. , 140000. , 580000. ,\n 235000. , 544000. , 248000. , 785000. ,\n 667500. , 429900. , 165000. , 635000. ,\n 248000. , 199000. , 465425. , 150000. ,\n 360000. , 655000. , 530000. , 315000. ,\n 375000. , 553000. , 309950. , 396675. ,\n 395000. , 447000. , 502000. , 430277.777778,\n 584000. , 480000. , 527700. , 785000. ,\n 425000. , 950000. , 319950. , 550000. ,\n 735000. , 418500. , 679000. , 1050000. ,\n 3710000. , 239950. , 280000. , 2555000. ,\n 300000. , 407000. , 249950. , 355000. ,\n 499950. , 225000. , 475000. , 0. ,\n 765000. , 256500. , 571000. , 311100. ,\n 865000. , 249000. , 782000. , 950000. ,\n 464500. , 1215000. , 306000. , 1965221. ,\n 439950. , 713250. , 915000. , 325000. ,\n 888550. , 285000. , 1970000. , 352450. ,\n 385500. , 300000. , 230000. , 2400000. ,\n 431500. , 510000. , 234000. , 1250000. ,\n 2888000. , 695000. , 258000. , 465500. ,\n 347000. , 460000. , 464000. , 407500. ,\n 413000. , 299000. , 270000. , 660000. ,\n 553650. , 880000. , 287919.782609, 481015. ,\n 525000. , 740000. , 252000. , 540000. ,\n 280000. , 400000. , 307550. , 345000. ,\n 403950. , 255000. , 399950. , 886000. ,\n 439333.333333, 500000. , 278900. , 949880. ,\n 352000. , 538000. , 611000. , 390000. ,\n 352000. , 268971.875 , 400000. , 342500. ,\n 460000. , 430000. , 507000. , 364000. ,\n 754950. , 718500. , 309620. , 568000. ,\n 417000. , 1712500. , 337000. , 478000. ,\n 450000. , 275000. , 250000. , 345000. ,\n 0. , 458000. , 225000. , 660000. ,\n 624800. , 1795000. , 820000. , 485000. ,\n 385000. , 599000. , 558000. , 244615. ,\n 1000000. , 475000. , 670000. , 619500. ,\n 525000. , 280000. , 246000. , 445000. ,\n 392000. , 330000. , 640000. , 408900. ,\n 596165.428571, 835000. , 486000. , 380000. ,\n 127160. , 582800. , 1600000. , 902000. ,\n 250500. , 200000. , 362750. , 268500. ,\n 84350. , 455000. , 500000. , 805000. ,\n 253500. , 940000. , 335000. , 700000. ,\n 520500. , 450000. , 160000. , 368000. ,\n 850000. , 308000. , 540000. , 599000. ,\n 325000. , 625000. , 457500. , 330000. ,\n 269950. , 275000. , 1250000. , 725000. ,\n 252000. , 570000. , 825000. , 385000. ,\n 150000. , 615000. , 1320000. , 329995. ,\n 339950. , 379000. , 1350000. , 794154. ,\n 739000. , 764000. , 403000. , 299995. ,\n 430000. , 890000. , 300000. , 279000. ,\n 810000. , 875000. , 185000. , 2100000. ,\n 420000. , 599950. , 445838. , 585000. ,\n 525000. , 1100000. , 599000. , 602500. ,\n 660000. , 224000. , 200000. , 332000. ,\n 920000. , 531500. , 350000. , 585000. ,\n 1920000. , 800000. , 675000. , 310000. ,\n 451555. , 2367000. , 170000. , 370000. ,\n 355000. , 590000. , 465000. , 260000. ,\n 435000. , 376000. , 648000. , 1070000. ,\n 437000. , 885000. , 425000. , 244000. ,\n 360000. , 620000. , 210000. , 399950. ,\n 435000. , 550000. , 148000. , 380000. ,\n 469000. , 295000. , 760000. , 749950. ,\n 739900. , 590000. , 339000. , 940000. ,\n 390000. , 765000. , 212000. , 654000. ,\n 259500. , 965000. , 530000. , 479000. ,\n 450000. , 455000. , 380000. , 668500. ,\n 175000. , 599000. , 479000. , 315000. ,\n 926300. , 650000. , 382500. , 295000. ,\n 734990. , 1240000. , 540000. , 566000. ,\n 456000. , 442000. , 515000. , 1680000. ,\n 365000. , 250000. , 565000. , 540000. ,\n 402000. , 175000. , 465000. , 224000. ,\n 285000. , 365000. , 543000. , 384000. ,\n 800000. , 994000. , 440000. , 1400000. ,\n 243000. , 585000. , 523950. , 540500. ,\n 615000. , 230000. , 446000. , 254000. ,\n 885000. , 210000. , 945000. , 218000. ,\n 330000. , 502000. , 390000. , 683500. ,\n 243000. , 299950. , 212500. , 2560498.33333 ,\n 575000. , 567500. , 254000. , 550000. ,\n 339990. , 534640. , 630000. , 405125. ,\n 252750. , 210000. , 427500. , 648475. ,\n 322500. , 2384000. , 358000. , 489000. ,\n 600000. , 160000. , 450000. , 298450. ,\n 215000. , 206000. , 385000. , 512031. ,\n 160000. , 343566. , 365000. , 575000. ,\n 282508.888889, 432000. , 395000. , 237481.25 ,\n 735000. , 225500. , 300000. , 217500. ,\n 280000. , 1060000. , 585000. , 240500. ,\n 900000. , 1200000. , 425000. , 1127000. ,\n 465750. , 589000. , 605000. , 350000. ,\n 480000. , 445000. , 1415000. , 375900. ,\n 735000. , 220083.333333, 197500. , 200000. ,\n 308830.769231, 145000. , 425000. , 647500. ,\n 286800. , 310000. , 311000. , 262000. ,\n 620000. , 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,\n 568000. , 568000. , 755000. , 234975. ,\n 314950. , 330000. , 588000. , 600000. ,\n 565000. , 672500. , 651000. , 437500. ,\n 235867. , 250000. , 197500. , 445800. ,\n 583000. , 777000. , 749000. , 280000. ,\n 429000. , 650000. , 607500. , 1033888. ,\n 725000. , 530000. , 335000. , 970500. ,\n 756000. , 465000. , 830000. , 351250. ,\n 0. , 800000. , 474800. , 437500. ,\n 1240000. , 382000. , 377691. , 622500. ,\n 183000. , 460000. , 382500. , 895000. ,\n 150000. , 0. , 555000. , 700000. ,\n 180000. , 650000. , 372977. , 950100. ,\n 289659. , 736000. , 744500. , 578000. ,\n 492650. , 330000. , 1580000. , 925000. ,\n 290000. , 515000. , 409316. , 280000. ,\n 479000. , 309000. , 290000. , 269000. ,\n 1309500. , 700000. , 395000. , 445700. ,\n 535000. , 749950. , 685000. , 790000. ,\n 661254. , 342246.428571, 475000. , 110000. ,\n 577000. , 1636000. , 270000. , 0. ,\n 720000. , 192500. , 560000. , 554000. ,\n 461000. , 580000. , 175000. , 320000. ,\n 219950. , 402500. , 375000. , 328000. ,\n 1250000. , 243000. , 452000. , 264000. ,\n 255000. , 400000. , 328950. , 854000. ,\n 460000. , 1165000. , 154950. , 0. ,\n 250600. , 410000. , 839900. , 291500. ,\n 1195000. , 2700000. , 625000. , 291000. ,\n 310000. , 165050. , 850000. , 762300. ,\n 549000. , 720000. , 220000. , 382500. ,\n 537500. , 397500. , 575000. , 295000. ,\n 627000. , 767450. , 890000. , 607000. ,\n 341000. , 186000. , 438750. , 404000. ,\n 852880. , 435000. , 626000. , 1370000. ,\n 282000. , 424500. , 462000. , 234000. ,\n 560000. , 2400000. , 254000. , 385000. ,\n 425000. , 400000. , 357500. , 300000. ,\n 800000. , 355000. , 350000. , 277000. ,\n 608000. , 215000. , 960000. , 725000. ,\n 685000. , 2110000. , 947500. , 895000. ,\n 840000. , 371000. , 324000. , 963000. ,\n 788000. , 470000. , 660000. , 563000. ,\n 480000. , 530000. , 690000. , 449990. ,\n 7800. , 840000. , 620000. , 963000. ,\n 175000. , 436000. , 757000. , 548000. ,\n 950000. , 355000. , 499000. , 485000. ,\n 408000. , 490000. , 230000. , 250000. ,\n 386591. , 370000. , 230000. , 150000. ,\n 1288333.33333 , 680000. , 585000. , 315000. ,\n 2280000. , 330000. , 850000. , 432000. ,\n 330000. , 630000. , 565000. , 723243.75 ,\n 265000. , 835000. , 350000. , 710000. ,\n 483945. , 1300000. , 599999. , 747500. ,\n 420000. , 236000. , 410000. , 235000. ,\n 288790. , 405000. , 377500. , 398000. ,\n 589500. , 220000. , 305000. , 396166.666667,\n 1038000. , 690000. , 763101. , 285000. ,\n 806000. , 362300. , 355000. , 569950. ,\n 507500. , 317061.875 , 245000. , 619850. ,\n 497333.333333, 397990. , 800866. , 525000. ,\n 310000. , 170000. , 300000. , 620000. ,\n 425000. , 234000. , 83300. , 305000. ,\n 305000. , 464900. , 902000. , 375000. ,\n 329950. , 3000000. , 245100. , 773000. ,\n 373500. , 560000. , 235000. , 229629.5 ,\n 324000. , 560000. , 345000. , 253000. ,\n 1070000. , 260000. , 355000. , 1225000. ,\n 660000. , 387884.615385, 575000. , 417250. ,\n 215000. , 492000. , 349000. , 1110000. ,\n 205000. , 650000. , 659950. , 429900. ,\n 550000. , 790000. , 290000. , 522000. ,\n 529000. , 219900. , 470000. , 257500. ,\n 315000. , 215000. , 1730000. , 499000. ,\n 448000. , 870000. , 419000. , 157500. ,\n 648000. , 405000. , 524950. , 390000. ,\n 580379. , 210000. , 237333.333333, 760000. ])"},"metadata":{}}]},{"cell_type":"code","source":"print(type(X_train),type(Y_train),type(X_test),type(Y_test))\n","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.912926Z","iopub.execute_input":"2023-05-23T19:58:29.913294Z","iopub.status.idle":"2023-05-23T19:58:29.919888Z","shell.execute_reply.started":"2023-05-23T19:58:29.913261Z","shell.execute_reply":"2023-05-23T19:58:29.918641Z"},"trusted":true},"execution_count":22,"outputs":[{"name":"stdout","text":" \n","output_type":"stream"}]},{"cell_type":"code","source":"train_houseprice=X_train.join(Y_train)\n","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.921541Z","iopub.execute_input":"2023-05-23T19:58:29.921946Z","iopub.status.idle":"2023-05-23T19:58:29.964958Z","shell.execute_reply.started":"2023-05-23T19:58:29.921898Z","shell.execute_reply":"2023-05-23T19:58:29.960079Z"},"trusted":true},"execution_count":23,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[23], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m train_houseprice\u001b[38;5;241m=\u001b[39m\u001b[43mX_train\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m(Y_train)\n","\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'join'"],"ename":"AttributeError","evalue":"'numpy.ndarray' object has no attribute 'join'","output_type":"error"}]},{"cell_type":"code","source":"train_houseprice","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.966294Z","iopub.status.idle":"2023-05-23T19:58:29.966729Z","shell.execute_reply.started":"2023-05-23T19:58:29.966531Z","shell.execute_reply":"2023-05-23T19:58:29.966551Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"raw","source":"arr1=np.array([[3.0, 2.25, 1620, ..., 2009, 0, 'USA'],\n [3.0, 2.25, 1820, ..., 1983, 2009, 'USA'],\n [4.0, 1.75, 1750, ..., 1926, 0, 'USA'],\n ...,\n [4.0, 2.5, 2070, ..., 2004, 2003, 'USA'],\n [4.0, 2.0, 1650, ..., 1955, 2005, 'USA'],\n [4.0, 3.5, 3770, ..., 2008, 0, 'USA']])\n ","metadata":{}},{"cell_type":"code","source":"arr2=np.array([ 430277.777778, 489950. , 460000. , ...,\n 343000. , 575000. , 1020000. ])","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.968722Z","iopub.status.idle":"2023-05-23T19:58:29.969211Z","shell.execute_reply.started":"2023-05-23T19:58:29.968998Z","shell.execute_reply":"2023-05-23T19:58:29.969021Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"arr = np.concatenate((arr1, arr2), axis=1)\n","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.970297Z","iopub.status.idle":"2023-05-23T19:58:29.970896Z","shell.execute_reply.started":"2023-05-23T19:58:29.970659Z","shell.execute_reply":"2023-05-23T19:58:29.970682Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"houseprice.drop(['date','city','street','statezip','condition'], axis=1)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.973484Z","iopub.status.idle":"2023-05-23T19:58:29.973912Z","shell.execute_reply.started":"2023-05-23T19:58:29.973713Z","shell.execute_reply":"2023-05-23T19:58:29.973732Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"houseprice.corr()\n","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.975625Z","iopub.status.idle":"2023-05-23T19:58:29.976217Z","shell.execute_reply.started":"2023-05-23T19:58:29.975917Z","shell.execute_reply":"2023-05-23T19:58:29.975963Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(15,8))\nsns.heatmap(houseprice.corr(),annot=True,cmap=\"YlGnBu\")","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.977800Z","iopub.status.idle":"2023-05-23T19:58:29.978394Z","shell.execute_reply.started":"2023-05-23T19:58:29.978107Z","shell.execute_reply":"2023-05-23T19:58:29.978133Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"houseprice.hist(figsize=(15,8))\n","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.980346Z","iopub.status.idle":"2023-05-23T19:58:29.980899Z","shell.execute_reply.started":"2023-05-23T19:58:29.980617Z","shell.execute_reply":"2023-05-23T19:58:29.980644Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.regplot(x=\"waterfront\", y=\"price\",data=houseprice)\nplt.ylim(0,)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.982816Z","iopub.status.idle":"2023-05-23T19:58:29.983234Z","shell.execute_reply.started":"2023-05-23T19:58:29.983039Z","shell.execute_reply":"2023-05-23T19:58:29.983058Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nwf=houseprice['waterfront']\nprice=houseprice['price']\nsns.boxplot(x=wf,y=price)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.984556Z","iopub.status.idle":"2023-05-23T19:58:29.985011Z","shell.execute_reply.started":"2023-05-23T19:58:29.984774Z","shell.execute_reply":"2023-05-23T19:58:29.984795Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.regplot(x=\"sqft_above\", y=\"price\",data=houseprice)\nplt.ylim(0,)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.986136Z","iopub.status.idle":"2023-05-23T19:58:29.986560Z","shell.execute_reply.started":"2023-05-23T19:58:29.986346Z","shell.execute_reply":"2023-05-23T19:58:29.986366Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sqf=houseprice['sqft_above']\nprice=houseprice['price']\nsns.boxplot(x=sqf,y=price)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.987716Z","iopub.status.idle":"2023-05-23T19:58:29.988162Z","shell.execute_reply.started":"2023-05-23T19:58:29.987943Z","shell.execute_reply":"2023-05-23T19:58:29.987972Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.regplot(x=\"bedrooms\", y=\"price\",data=houseprice)\nplt.ylim(0,)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.990044Z","iopub.status.idle":"2023-05-23T19:58:29.990500Z","shell.execute_reply.started":"2023-05-23T19:58:29.990297Z","shell.execute_reply":"2023-05-23T19:58:29.990317Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"br=houseprice['bedrooms']\nprice=houseprice['price']\nsns.boxplot(x=br,y=price)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.992131Z","iopub.status.idle":"2023-05-23T19:58:29.992582Z","shell.execute_reply.started":"2023-05-23T19:58:29.992367Z","shell.execute_reply":"2023-05-23T19:58:29.992388Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.regplot(x=\"bathrooms\", y=\"price\",data=houseprice)\nplt.ylim(0,)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.994346Z","iopub.status.idle":"2023-05-23T19:58:29.995123Z","shell.execute_reply.started":"2023-05-23T19:58:29.994872Z","shell.execute_reply":"2023-05-23T19:58:29.994894Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"b=houseprice['bathrooms']\nprice=houseprice['price']\nsns.boxplot(x=b,y=price)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.997383Z","iopub.status.idle":"2023-05-23T19:58:29.997788Z","shell.execute_reply.started":"2023-05-23T19:58:29.997598Z","shell.execute_reply":"2023-05-23T19:58:29.997616Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.regplot(x=\"sqft_living\", y=\"price\",data=houseprice)\nplt.ylim(0,)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:29.998793Z","iopub.status.idle":"2023-05-23T19:58:29.999241Z","shell.execute_reply.started":"2023-05-23T19:58:29.999017Z","shell.execute_reply":"2023-05-23T19:58:29.999036Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sqfl=houseprice['sqft_living']\nprice=houseprice['price']\nsns.boxplot(x=sqfl,y=price)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:30.000682Z","iopub.status.idle":"2023-05-23T19:58:30.001345Z","shell.execute_reply.started":"2023-05-23T19:58:30.001008Z","shell.execute_reply":"2023-05-23T19:58:30.001056Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.regplot(x=\"sqft_lot\", y=\"price\",data=houseprice)\nplt.ylim(0,)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:30.002738Z","iopub.status.idle":"2023-05-23T19:58:30.003377Z","shell.execute_reply.started":"2023-05-23T19:58:30.002968Z","shell.execute_reply":"2023-05-23T19:58:30.002987Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sqft=houseprice['sqft_lot']\nprice=houseprice['price']\nsns.boxplot(x=sqft,y=price)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:30.005043Z","iopub.status.idle":"2023-05-23T19:58:30.005487Z","shell.execute_reply.started":"2023-05-23T19:58:30.005288Z","shell.execute_reply":"2023-05-23T19:58:30.005308Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.regplot(x=\"floors\", y=\"price\",data=houseprice)\nplt.ylim(0,)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:30.007858Z","iopub.status.idle":"2023-05-23T19:58:30.008443Z","shell.execute_reply.started":"2023-05-23T19:58:30.008208Z","shell.execute_reply":"2023-05-23T19:58:30.008230Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"f=houseprice['floors']\nprice=houseprice['price']\nsns.boxplot(x=f,y=price)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:30.011023Z","iopub.status.idle":"2023-05-23T19:58:30.011562Z","shell.execute_reply.started":"2023-05-23T19:58:30.011319Z","shell.execute_reply":"2023-05-23T19:58:30.011342Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.regplot(x=\"view\", y=\"price\",data=houseprice)\nplt.ylim(0,)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:30.013365Z","iopub.status.idle":"2023-05-23T19:58:30.013791Z","shell.execute_reply.started":"2023-05-23T19:58:30.013591Z","shell.execute_reply":"2023-05-23T19:58:30.013610Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"v=houseprice['view']\nprice=houseprice['price']\nsns.boxplot(x=v,y=price)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:30.015156Z","iopub.status.idle":"2023-05-23T19:58:30.015567Z","shell.execute_reply.started":"2023-05-23T19:58:30.015373Z","shell.execute_reply":"2023-05-23T19:58:30.015391Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.regplot(x=\"condition\", y=\"price\",data=houseprice)\nplt.ylim(0,)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:30.017298Z","iopub.status.idle":"2023-05-23T19:58:30.017765Z","shell.execute_reply.started":"2023-05-23T19:58:30.017540Z","shell.execute_reply":"2023-05-23T19:58:30.017560Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"c=houseprice['condition']\nprice=houseprice['price']\nsns.boxplot(x=c,y=price)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:30.020195Z","iopub.status.idle":"2023-05-23T19:58:30.020731Z","shell.execute_reply.started":"2023-05-23T19:58:30.020423Z","shell.execute_reply":"2023-05-23T19:58:30.020441Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.regplot(x=\"sqft_above\", y=\"price\",data=houseprice)\nplt.ylim(0,)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:30.021703Z","iopub.status.idle":"2023-05-23T19:58:30.022591Z","shell.execute_reply.started":"2023-05-23T19:58:30.022358Z","shell.execute_reply":"2023-05-23T19:58:30.022382Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sqfa=houseprice['sqft_above']\nprice=houseprice['price']\nsns.boxplot(x=sqfa,y=price)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:30.025517Z","iopub.status.idle":"2023-05-23T19:58:30.026229Z","shell.execute_reply.started":"2023-05-23T19:58:30.025770Z","shell.execute_reply":"2023-05-23T19:58:30.025789Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.regplot(x=\"sqft_basement\", y=\"price\",data=houseprice)\nplt.ylim(0,)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:30.028093Z","iopub.status.idle":"2023-05-23T19:58:30.028595Z","shell.execute_reply.started":"2023-05-23T19:58:30.028359Z","shell.execute_reply":"2023-05-23T19:58:30.028382Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sqfb=houseprice['sqft_basement']\nprice=houseprice['price']\nsns.boxplot(x=sqfb,y=price)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:30.029842Z","iopub.status.idle":"2023-05-23T19:58:30.030370Z","shell.execute_reply.started":"2023-05-23T19:58:30.030146Z","shell.execute_reply":"2023-05-23T19:58:30.030170Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.regplot(x=\"yr_built\", y=\"price\",data=houseprice)\nplt.ylim(0,)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:30.031727Z","iopub.status.idle":"2023-05-23T19:58:30.032228Z","shell.execute_reply.started":"2023-05-23T19:58:30.032000Z","shell.execute_reply":"2023-05-23T19:58:30.032024Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"yb=houseprice['yr_built']\nprice=houseprice['price']\nsns.boxplot(x=yb,y=price)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:30.033739Z","iopub.status.idle":"2023-05-23T19:58:30.034215Z","shell.execute_reply.started":"2023-05-23T19:58:30.034002Z","shell.execute_reply":"2023-05-23T19:58:30.034024Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.regplot(x=\"yr_renovated\", y=\"price\",data=houseprice)\nplt.ylim(0,)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:30.035550Z","iopub.status.idle":"2023-05-23T19:58:30.035995Z","shell.execute_reply.started":"2023-05-23T19:58:30.035772Z","shell.execute_reply":"2023-05-23T19:58:30.035790Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"yr=houseprice['yr_renovated']\nprice=houseprice['price']\nsns.boxplot(x=yr,y=price)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:30.038157Z","iopub.status.idle":"2023-05-23T19:58:30.038628Z","shell.execute_reply.started":"2023-05-23T19:58:30.038397Z","shell.execute_reply":"2023-05-23T19:58:30.038419Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#MULTIPLE LINEAR REGRESSION MODEL","metadata":{"execution":{"iopub.status.busy":"2023-05-23T19:58:30.041103Z","iopub.status.idle":"2023-05-23T19:58:30.041562Z","shell.execute_reply.started":"2023-05-23T19:58:30.041339Z","shell.execute_reply":"2023-05-23T19:58:30.041360Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn import linear_model\nX1 = houseprice[['bedrooms', 'bathrooms','sqft_living','sqft_lot','view','condition','floors','sqft_above','sqft_basement','yr_built','yr_renovated']]\nY1 = houseprice['price']\n\nregr = linear_model.LinearRegression()\nregr.fit(X1,Y1)\nprint(regr)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T20:02:39.636917Z","iopub.execute_input":"2023-05-23T20:02:39.637719Z","iopub.status.idle":"2023-05-23T20:02:39.654223Z","shell.execute_reply.started":"2023-05-23T20:02:39.637679Z","shell.execute_reply":"2023-05-23T20:02:39.651989Z"},"trusted":true},"execution_count":30,"outputs":[{"name":"stdout","text":"LinearRegression()\n","output_type":"stream"}]},{"cell_type":"code","source":"# Find the value of house price where 3.0\t1.50\t1340\t7912\t1.5\t0\t0\t3\t1340\t0\t1955\t2005\npredictedY1 = regr.predict([[3.0,1.50,1340,7912,1.50,3,0,1340,0,1955,2005]])\n\nprint(predictedY1)","metadata":{"execution":{"iopub.status.busy":"2023-05-23T20:39:47.473339Z","iopub.execute_input":"2023-05-23T20:39:47.474344Z","iopub.status.idle":"2023-05-23T20:39:47.481122Z","shell.execute_reply.started":"2023-05-23T20:39:47.474303Z","shell.execute_reply":"2023-05-23T20:39:47.480007Z"},"trusted":true},"execution_count":32,"outputs":[{"name":"stdout","text":"[388965.34637187]\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n warnings.warn(\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}