def clean_up1(ds, rs):
ds = ds.copy()
del ds["id"]
del ds["date"]
del ds["sqft_living"]
del ds["sqft_lot"]
del ds["view"]
del ds["lat"]
del ds["long"]
ds["age"] = 2018 - np.where(ds["yr_renovated"] == 0, ds["yr_built"],
ds["yr_renovated"])
del ds["yr_built"]
del ds["yr_renovated"]
ds["bathrooms"] = ds["bathrooms"] * ds["bedrooms"]
ds = hskc_functions.createGroups(ds, "bedrooms")
ds = hskc_functions.createGroups(ds, "bathrooms")
ds = hskc_functions.createGroups(ds, "floors")
ds = hskc_functions.dummifyField(ds, "zipcode")
ds = hskc_functions.createQuantile(ds, "sqft_living15")
#ds= createQuantile(ds,"sqft_above")
result = hskc_functions.runRegression(ds)
result.update({
"Clean Up Id":
"1",
"Comments":
"Removed Id, date, sqft_living, sqft_lot,view, lat, long, yr_built, year_renovated\n"
+ "Created age\n" +
"Updated bathrooms=bathrooms*bedrooms\nGrouped bedrooms,bathrooms,floors,\n"
+ "Dummify zipcode\n" + "Quantiled sqft_living15"
})
rs = rs.append(result, ignore_index=True)
return ds, rs
def clean_up2(ds, rs):
ds = ds.copy()
del ds["id"]
del ds["date"]
del ds["sqft_living"]
del ds["sqft_lot"]
del ds["view"]
del ds["lat"]
del ds["long"]
#Making zipcode as cities
zc = pd.read_csv(
"C:\\Users\\manoj\\Documents\\Acadgild DSB\\Kaggle Project\\House Sales in King County\\Data\\zipcode.csv"
)
ds = ds.merge(zc[["zipcode", "City"]], on="zipcode")
del ds["zipcode"]
ds = hskc_functions.dummifyField(ds, "City")
ds["age"] = 2018 - np.where(ds["yr_renovated"] == 0, ds["yr_built"],
ds["yr_renovated"])
del ds["yr_built"]
del ds["yr_renovated"]
ds["bathrooms"] = ds["bathrooms"] * ds["bedrooms"]
ds = hskc_functions.createGroups(ds, "bedrooms")
ds = hskc_functions.createGroups(ds, "bathrooms")
ds = hskc_functions.createGroups(ds, "floors")
#ds=hskc_functions.dummifyField(ds,"zipcode")
ds = hskc_functions.createQuantile(ds, "sqft_living15")
#ds= createQuantile(ds,"sqft_above")
result = hskc_functions.runRegression(ds)
result.update({
"Clean Up Id":
"2",
"Comments":
"Removed Id, date, sqft_living, sqft_lot,view, lat, long, yr_built, year_renovated, zipcode\n"
+ "Append City\n" + "Created age\n" +
"Updated bathrooms=bathrooms*bedrooms\nGrouped bedrooms,bathrooms,floors,\n"
+ "Dummify City\n" + "Quantiled sqft_living15"
})
rs = rs.append(result, ignore_index=True)
return ds, rs
def clean_up3(ds, rs):
ds = ds.copy()
del ds["id"]
del ds["date"]
del ds["sqft_living"]
del ds["sqft_lot"]
del ds["view"]
del ds["lat"]
del ds["long"]
ds["age"] = 2018 - np.where(ds["yr_renovated"] == 0, ds["yr_built"],
ds["yr_renovated"])
del ds["yr_built"]
del ds["yr_renovated"]
ds["bathrooms"] = ds["bathrooms"] * ds["bedrooms"]
ds = hskc_functions.createGroups(ds, "bedrooms")
ds = hskc_functions.createGroups(ds, "bathrooms")
ds = hskc_functions.createGroups(ds, "floors")
ds = hskc_functions.dummifyField(ds, "zipcode")
ds = hskc_functions.createQuantile(ds, "sqft_living15")
#Scaling entire data
x = ds.values #returns a numpy array
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
ds = pd.DataFrame(x_scaled)
#ds= createQuantile(ds,"sqft_above")
result = hskc_functions.runRegression(ds, True)
result.update({
"Clean Up Id":
"3",
"Comments":
"Removed Id, date, sqft_living, sqft_lot,view, lat, long, yr_built, year_renovated\n"
+ "Created age\n" +
"Updated bathrooms=bathrooms*bedrooms\nGrouped bedrooms,bathrooms,floors,\n"
+ "Dummify zipcode\n" + "Quantiled sqft_living15\nData is scaled"
})
rs = rs.append(result, ignore_index=True)
return ds, rs