Question 1: Data The car dealership collected data and would like to predict the price of a used car depending on the sp

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Question 1 Data The Car Dealership Collected Data And Would Like To Predict The Price Of A Used Car Depending On The Sp 1 (78.55 KiB) Viewed 36 times

Question 1: Data The car dealership collected data and would like to predict the price of a used car depending on the specific characteristics. Formulate this as machine learning problem: (a) What kind of ML problem is this and why? (b) List features and their types. (c) List target feature(s). (d) Which labels are used in this problem? (e) How many data points are in this dataset? (f) What algorithm could you apply to solve this problem? Car_Name Year Selling Price Present Price Kms_Driven Fuel Type Seller Type Transmission Owner ritz 2014 3.35 5.59 Petrol Dealer 1 sx4 2013 4.75 9.54 Diesel Dealer 2 ciaz 2017 7.25 9.85 Petrol Dealer 3 wagon r 2011 2.85 4.15 Petrol Dealer swift 2014 4.60 6.87 Diesel Dealer E 296 297 298 299 300 city 2016 brio 2015 city 2009 city 2017 brio 2016 301 rows x 9 columns 2. *** 9.50 4.00 3.35 11.50 5.30 11.60 5.90 11.00 12.50 5.90 27000 43000 6900 5200 42450 33988 60000 87934 9000 5464 Diesel Petrol Petrol Diesel Petrol Dealer Dealer Dealer Dealer Dealer Manual Manual Manual Manual Manual Manual Manual Manual Manual Manual 0 0 0 a) Write the hypothesis and cost function for the problem stated above. b) Explain the gradient descent algorithm (write the pseudo code) and the purpose of a learning rate parameter a. c) If weights are initialized on 100, calculate their value after two iterations of gradient descent using one feature (kms_driven). d) Define everything used in the solution. 0 0 HE 0 0 0 0 0