Any efficient way to build non-linear regression model for polynomial features?












0












$begingroup$


I am trying to understand how crime frequency affect house price in certain area. To do so, I started with Chicago crime data and zillow real estate data. I want to understand the relation between house price and crime frequency and top 5 crimes in certain areas. Initially, I build up model for this specification, but it wasn't very meaningful to me. Can anyone enlighten me what should I do? any efficient approach to train regression model for potential relation between house price and crime frequency in certain areas? any heuristic idea to move forward?



example data snippet:



here is the merged data that includes annual house price and top crime type in certain areas:



example data



Here is reproducible example data snippet



my attempt



so here is my attempt to fit regression model with above reproducible example data:



from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
import pandas as pd

regDF = pd.read_csv('exampleDF')

X_feats = regDF.drop(['Avg_Price_2012'], axis=1)
y_label = regDF['Avg_Price_2012'].values

sc_x = StandardScaler()
sc_y = StandardScaler()
X = sc_x.fit_transform(X_feats)
#y= sc_y.fit_transform(y_label)
y = sc_y.fit_transform(y_label .reshape(-1,1)).flatten()
regModel = LinearRegression()
regModel.fit(X, y)
regModel.coef_


but to me, above model wasn't that efficient and needs to be done something more. I think I have to use non linear regression model for those polynomial features, and I am not sure to get this done.



Can anyone point me out how to build correct model for house price prediction over type of crimes and frequencies in certain areas? any idea? Thanks



Goal:



I want to build regression model to predict house price based on crime frequencies and types in certain areas. How can I get modeling the relationship between house price and crimes in certain areas? any thoughts?









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    0












    $begingroup$


    I am trying to understand how crime frequency affect house price in certain area. To do so, I started with Chicago crime data and zillow real estate data. I want to understand the relation between house price and crime frequency and top 5 crimes in certain areas. Initially, I build up model for this specification, but it wasn't very meaningful to me. Can anyone enlighten me what should I do? any efficient approach to train regression model for potential relation between house price and crime frequency in certain areas? any heuristic idea to move forward?



    example data snippet:



    here is the merged data that includes annual house price and top crime type in certain areas:



    example data



    Here is reproducible example data snippet



    my attempt



    so here is my attempt to fit regression model with above reproducible example data:



    from sklearn.linear_model import LinearRegression
    from sklearn.preprocessing import StandardScaler
    import pandas as pd

    regDF = pd.read_csv('exampleDF')

    X_feats = regDF.drop(['Avg_Price_2012'], axis=1)
    y_label = regDF['Avg_Price_2012'].values

    sc_x = StandardScaler()
    sc_y = StandardScaler()
    X = sc_x.fit_transform(X_feats)
    #y= sc_y.fit_transform(y_label)
    y = sc_y.fit_transform(y_label .reshape(-1,1)).flatten()
    regModel = LinearRegression()
    regModel.fit(X, y)
    regModel.coef_


    but to me, above model wasn't that efficient and needs to be done something more. I think I have to use non linear regression model for those polynomial features, and I am not sure to get this done.



    Can anyone point me out how to build correct model for house price prediction over type of crimes and frequencies in certain areas? any idea? Thanks



    Goal:



    I want to build regression model to predict house price based on crime frequencies and types in certain areas. How can I get modeling the relationship between house price and crimes in certain areas? any thoughts?









    share







    New contributor




    user88911 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$















      0












      0








      0





      $begingroup$


      I am trying to understand how crime frequency affect house price in certain area. To do so, I started with Chicago crime data and zillow real estate data. I want to understand the relation between house price and crime frequency and top 5 crimes in certain areas. Initially, I build up model for this specification, but it wasn't very meaningful to me. Can anyone enlighten me what should I do? any efficient approach to train regression model for potential relation between house price and crime frequency in certain areas? any heuristic idea to move forward?



      example data snippet:



      here is the merged data that includes annual house price and top crime type in certain areas:



      example data



      Here is reproducible example data snippet



      my attempt



      so here is my attempt to fit regression model with above reproducible example data:



      from sklearn.linear_model import LinearRegression
      from sklearn.preprocessing import StandardScaler
      import pandas as pd

      regDF = pd.read_csv('exampleDF')

      X_feats = regDF.drop(['Avg_Price_2012'], axis=1)
      y_label = regDF['Avg_Price_2012'].values

      sc_x = StandardScaler()
      sc_y = StandardScaler()
      X = sc_x.fit_transform(X_feats)
      #y= sc_y.fit_transform(y_label)
      y = sc_y.fit_transform(y_label .reshape(-1,1)).flatten()
      regModel = LinearRegression()
      regModel.fit(X, y)
      regModel.coef_


      but to me, above model wasn't that efficient and needs to be done something more. I think I have to use non linear regression model for those polynomial features, and I am not sure to get this done.



      Can anyone point me out how to build correct model for house price prediction over type of crimes and frequencies in certain areas? any idea? Thanks



      Goal:



      I want to build regression model to predict house price based on crime frequencies and types in certain areas. How can I get modeling the relationship between house price and crimes in certain areas? any thoughts?









      share







      New contributor




      user88911 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I am trying to understand how crime frequency affect house price in certain area. To do so, I started with Chicago crime data and zillow real estate data. I want to understand the relation between house price and crime frequency and top 5 crimes in certain areas. Initially, I build up model for this specification, but it wasn't very meaningful to me. Can anyone enlighten me what should I do? any efficient approach to train regression model for potential relation between house price and crime frequency in certain areas? any heuristic idea to move forward?



      example data snippet:



      here is the merged data that includes annual house price and top crime type in certain areas:



      example data



      Here is reproducible example data snippet



      my attempt



      so here is my attempt to fit regression model with above reproducible example data:



      from sklearn.linear_model import LinearRegression
      from sklearn.preprocessing import StandardScaler
      import pandas as pd

      regDF = pd.read_csv('exampleDF')

      X_feats = regDF.drop(['Avg_Price_2012'], axis=1)
      y_label = regDF['Avg_Price_2012'].values

      sc_x = StandardScaler()
      sc_y = StandardScaler()
      X = sc_x.fit_transform(X_feats)
      #y= sc_y.fit_transform(y_label)
      y = sc_y.fit_transform(y_label .reshape(-1,1)).flatten()
      regModel = LinearRegression()
      regModel.fit(X, y)
      regModel.coef_


      but to me, above model wasn't that efficient and needs to be done something more. I think I have to use non linear regression model for those polynomial features, and I am not sure to get this done.



      Can anyone point me out how to build correct model for house price prediction over type of crimes and frequencies in certain areas? any idea? Thanks



      Goal:



      I want to build regression model to predict house price based on crime frequencies and types in certain areas. How can I get modeling the relationship between house price and crimes in certain areas? any thoughts?







      machine-learning python





      share







      New contributor




      user88911 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.










      share







      New contributor




      user88911 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.








      share



      share






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      user88911 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      asked 1 min ago









      user88911user88911

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      New contributor




      user88911 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.





      New contributor





      user88911 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






      user88911 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






















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