I have depression

py_scripts/scout.py

@@ -1,8 +1,9 @@
 # Regression/Prediction (Totally gonna do later trust bro)
 from sklearn.linear_model import LinearRegression
-
 from sklearn.impute import SimpleImputer
+from sklearn import preprocessing
 import numpy as np
+import pandas as pd
 
 
 def cure_depression(dataset):
@@ -16,6 +17,7 @@ def cure_depression(dataset):
     return dataset
 
 
+# Fuck you and whateevr you fucking stand for you dumb whore faggot
 def regression_expression(dataset, column, missing_value):
     lr = LinearRegression()
     numeric = dataset.select_dtypes(include=np.number)
@@ -32,9 +34,21 @@ def regression_expression(dataset, column, missing_value):
     y = traindf[column]
     traindf.drop(column, axis=1, inplace=True)
     lr.fit(traindf, y)
-    testdf.drop(column, axis=1, inplace=True)
     pred = lr.predict(testdf)
     # can't put this in data set directly because length no match
     # join testdf and traindf to form dataset perhaps??
     testdf[column] = pred
     print(testdf.head(30))
+
+# https://scikit-learn.org/stable/modules/preprocessing.html#preprocessing
+# That helps ^
+# This boi should work, idk i'm implementing blindly
+def scaling_zscore(datashitter, col):
+    scaler = preprocessing.StandardScaler().fit(datashitter[col])
+    return scaler.transform(datashitter[col])
+
+def scaling_range(datashitter, col):
+    nonnull = datashitter[col].isna()
+    minmax_scaler = preprocessing.MinMaxScaler()
+    trainer = minmax_scaler.fit_transform(datashitter[nonnull])
+    return minmax_scaler.transform(datashitter[col])