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 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) & (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?
Any help and I am grateful already. Cheers people.