scrappy code testing out modelling affect of thermal transmittance on EPC

model_data/app.py

@@ -237,6 +237,7 @@ def handler():
         "built-form",
         # "construction-age-band",
         "number-habitable-rooms",
+        "constituency",
     ]
 
     component_features = [
@@ -245,8 +246,43 @@ def handler():
     ]
 
     model_data = df[[response] + component_features + base_features]
-    model_data = model_data.reset_index()
+    model_data = model_data.reset_index(drop=True)
     model_data["idx"] = model_data.index.copy()
+
+    # Append on u-value estimates
+    model_data = model_data.merge(
+        pd.DataFrame(cleaner.cleaned["walls-description"])[["original_description", "thermal_transmittance"]],
+        how="left",
+        left_on="walls-description",
+        right_on="original_description"
+    )
+    # Take just entries with U-values
+    model_data = model_data[~pd.isnull(model_data["thermal_transmittance"])]
+
+    # We need to split the data into a train and test set for model build
+
+    import statsmodels.api as sm
+
+    # Assuming 'df' is your DataFrame
+    X = model_data[base_features + ["thermal_transmittance"]]
+    Y = model_data[response]
+
+    # Add a constant to the independent value
+    X1 = sm.add_constant(X)
+
+    # make regression model
+    model = sm.OLS(Y, X1)
+
+    # fit model and print results
+    results = model.fit()
+    print(results.summary())
+
+    model_data[["thermal_transmittance", response]].corr()
+
+    summary = model_data.groupby(["property-type", "built-form"], observed=True)[
+        ["thermal_transmittance", response]
+    ].corr()
+
     summary = (
         model_data
         .groupby(component_features + base_features)

model_data/requirements.txt

@@ -14,4 +14,5 @@ pyproj
 pint
 geopandas
 mip
-seaborn
+seaborn
+statsmodels