I was trying to build a prediction system where I have the input data arranged in multiple columns. The input data would be of the type where I have
- weather,
- service type (bronze, silver, gold),
- size (xs, s, m, l, xl, xxl),
- time,
- availability,
- pin code, and
- the result (target).
Each of the data types is arranged in columns with a specific code. I have read this, this, this , this, and this.
They are helpful but do not give me a clear picture. I would like to achieve a multi-vs-one prediction. Most of the schemes available are one-vs-one where the data is a 1*1 entity.
Here is a sample code that I was working with:
regressionModel = linear_model.LinearRegression()
""" 3. Processing is not necessary for current concept """
y = pd.DataFrame(modifiedDFSet['Code'])
print(y.shape)
drop2 = ['Code']
X = modifiedDFSet.drop(drop2)
print(X.shape)
""" 4. Data Scaling, Data Imputation is not necessary. Training and Test data is prepared using train-test-split """
train_data, test_data = train_test_split(X, test_size=0.20, random_state=42)
""" 5. the Regression Model """
# h = .02 # step size in the mesh
# logreg = linear_model.LinearRegression()
# we create an instance of Neighbours Classifier and fit the data.
regressionModel.fit(X, y)
d_predictions = regressionModel.predict(y)
X.shape and y.shape would yield (500, 6) and (500, 1), respectively, which would obviously cause a dimensional error in the d_predictions, meaning the regression model does not take multiple column inputs.
I have a hypothesis that I can create a scoring scheme that will take into account the importance of each of the columns and create a scheme that creates a score and the end result would be a one-vs-one regression problem. Looking for some direction with respect to my hypothesis. Is it correct, wrong or halfway?
