Question 1: Rental Bike Prediction
This data The data is found in the file bike.csv, with the following columns:
1: year: The first or second year of the rental company
2: mnth: The month of the year (0 = Jan, 12 = Dec)
3: hr: The hour of the day (from 0 = midnight to 23 = 11PM)
4: weekday: The day of the week (0 = Monday, 6 = Sunday)
5: temp: Normalized temperature in Celsius. 6: atemp: Normalized feeling temperature in Celsius
7: hum: Normalized humidity.
8: windspeed: Normalized wind speed.
9: cnt: What you are trying to predict. Count of total rental bikes
Use KNN to predict the column cnt. You should include what F and K were, some measures overall error in prediction, etc. Describe which values were difficult to predict.
Source: Hadi Fanaee-T , Laboratory of Artificial Intelligence and Decision Support (LIAAD), University of Porto, INESC Porto, Campus da FEUP, PRua Dr. Roberto Frias, 378 ,4200 - 465 Porto, Portugal

Question 2 : Breast Cancer Prediction
This data contains various measurements on a normalized 1-10 scale of cells (or tumors) of women being tested for breast cancer. The data is found in the file BC.csv, with the following columns:
1: Thick: Clump Thickness
2: Usize: Uniformity of Cell Size
3: Ushape: Uniformity of Cell Shape
4: Madhe: Marginal Adhesion
5: SECsize: Single Epithelial Cell Size
6: Bchrom: Bland Chromatin
7: NormNu: Normal Nucleoli
8: Mitos: Mitoses
9: Tumor: What you are trying to predict. Benign, or Malignant.
Use KNN to predict the column Tumor. You should include what F and K were, some measures overall error in prediction, etc. Describe which values were difficult to predict.
Creator: Dr. WIlliam H. Wolberg (physician) ,University of Wisconsin Hospitals, Madison, Wisconsin, USA
Topic II, Question 1: Breast Cancer Prediction
Use the same data as in Topic I, question 1.
Use classification tree to predict the column Tumor. You should show an image of your tree, some error rates, etc. Describe which variables were important to the tree.

The Report Format
Each question should be a short report. This means you write in full sentences, and have the following sections for each question, while being as specific as you can about your results:
I. Introduction. State the question you are trying to answer, why it is a question of interest (why might we be interested in the answer), and what approach you are going to take (just the name of the approach).
II. Summary of your data (and only the data you are using for the question). This should include things like plots (histograms, boxplots) including the interpretation of the plots, and summary values such as sample means and standard deviations. This is where you should justify which non-parametric technique you are using. An R handout is available online for graphing and summaries of various data types.
III. Analysis. Report back confidence intervals, test-statistics, and p-values, nulls and alternatives, if appropriate. You may use tables here, but be sure that you organize your work. Remember to write your results in full sentences where possible.
IV. Interpretation. State your conclusion, and inference that you may draw from your corresponding tests or confidence intervals. These should all be in terms of your problem.
V. Conclusion. Summarize briefly your findings. Here you do not have to re-iterate your numeric values, but summarize all relevant conclusions.

Question 1: Rental Bike Prediction This data The data is found in...