Fix and complete Python Coding

Fix and complete Python coding for 3 portfolio tasks. Details on each portfolio task provided below: **Task 1:** Files required: 1. [login to view URL] (Calculation of the field elevation_gain - This is the metres climbed or descended between each observation) 2. [login to view URL] 3. [login to view URL] 4. [login to view URL] 5. [login to view URL] 6. [login to view URL] Requirements: 1. What is the overall distance travelled for each of the rides? What are the average speeds etc. Provide a summary for each ride. 2. Compare the range of speeds for each ride, are time trials faster than road races? 3. Compare the speeds achieved in the two time trials (three years apart). As well as looking at the averages, can you see where in the ride one or the other is faster. 4. From the elevation and distance variable you can calculate gradient to see whether the rider is climbing, descending or on the flat. Use this to calculate the average speeds in those three cases (climbing, flat or descending). Note that flat might not be zero gradient but might allow for slight climbs and falls. Description of Fields: index is a datetime showing the time that the observation was made (I wasn't riding at night, this is converted to UTC) Latitude, longitude, elevation from the GPS, the position of the rider at each timepoint Temperature - The current ambient temperature in degrees celcius Power - The power being generated by the rider in Watts Cadence - The rotational speed of the pedals in revolutions per second Hr - Heart rate in beats per minute Distance - Distance travelled between observations in km Speed - Speed measured in km/h **Task 2:** Files required: 1. [login to view URL] 2. [login to view URL] 3. [login to view URL] Requirements: 1. Describe the distribution of vouchers by: State, Sport - which states/sports stand out? 2. Are some sports more popular in different states? 3. Are any electorates over/under represented in their use of vouchers? 4. Is there a relationship between any of the SEIFA measures and voucher use in an LGA? **Task 3:** Files required: 1. [login to view URL] 2. [login to view URL] Requirements: - Build and evaluate a Logistic Predictive Model for churn by predicting the value of the CHURN_IND field in the data from some of the other fields. - Look for significant clusters within the churn data either by looking at those who churn separately and those who don't or group them all together.