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cs401r_w2016:lab12 [2018/04/11 16:51] sadler [Hints:] |
cs401r_w2016:lab12 [2021/06/30 23:42] (current) |
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| - A notebook containing your code, but we will not run it. | - A notebook containing your code, but we will not run it. | ||
| - A set of predictions for a specific list of <user,movie> pairs, in a CSV file. | - A set of predictions for a specific list of <user,movie> pairs, in a CSV file. | ||
| - | - A report discussing your approach, how well it worked (in terms of RMSE), and any visualizations or patterns you found in the data. PDF format, please! | + | - A report discussing your approach, how well it worked (in terms of RMSE), and any visualizations or patterns you found in the data. Markdown format, please!! |
| We will run a small "competition" on your predictions: the three students with the best predictions will get 10% extra credit on this lab. | We will run a small "competition" on your predictions: the three students with the best predictions will get 10% extra credit on this lab. | ||
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| Your entry will be graded on the following elements: | Your entry will be graded on the following elements: | ||
| - | * 100% Project writeup | + | * 85% Project writeup |
| - | * 35% Exploratory data analysis | + | * 30% Exploratory data analysis |
| - | * 35% Description of technical approach | + | * 30% Description of technical approach |
| - | * 30% Analysis of performance of method | + | * 25% Analysis of performance of method |
| + | * 15% Submission of predictions csv file | ||
| * 10% extra credit for the three top predictions | * 10% extra credit for the three top predictions | ||
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| <code python> | <code python> | ||
| + | |||
| + | import numpy as np | ||
| + | import pandas as pd | ||
| pred_array = pd.read_table('predictions.dat') | pred_array = pd.read_table('predictions.dat') | ||
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| my_preds = np.zeros((N,1)) | my_preds = np.zeros((N,1)) | ||
| - | for id in range(N): ### Makeyour predictions | + | for id in range(N): ### Prediction loop |
| predicted_rating = 3 | predicted_rating = 3 | ||
| my_preds[ id, 0 ] = predicted_rating ### This Predicts everything as 3 | my_preds[ id, 0 ] = predicted_rating ### This Predicts everything as 3 | ||