Q1) (Heart Failure Prediction Dataset) contains 11 clinical features for predicting heart disease events. This dataset was created by combining different datasets already available independently but not combined before. In this dataset, 5 heart datasets are combined over 11 common features which makes it the largest heart disease dataset available so far for research purposes. Attribute Information 1. Age: age of the patient [years] 2. Sex: sex of the patient [M: Male, F: Female] 3. ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic] 4. RestingBP: resting blood pressure [mm Hg] 5. Cholesterol: serum cholesterol [mm/dl] 6. FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise] 7. RestingECG: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria] 8. MaxHR: maximum heart rate achieved [Numeric value between 60 and 202] 9. ExerciseAngina: exercise-induced angina [Y: Yes, N: No] 10. Oldpeak: oldpeak = ST [Numeric value measured in depression] 11. ST_Slope: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping] 12. HeartDisease: output class [1: heart disease, 0: Normal] Introduction To Data Science Using this CSV file do the following: 1) pre-processing the data. a. Splitting of the data set in Training and testing sets b. Taking care of Missing values c. Taking care of Categorical Features d. Normalization of data set 2) Ask and answer at least 4 questions about the data , such as : What is percentage of women with heart disease in the database