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Data Analysis Resources Kaggle Machine Learning

Facebook Data Analysis

In [20]: import pandas as pd import numpy as np In [ ]: # Take few samples for the visualization sample_fbcheckin_train_tbl = fbcheckin_train_tbl[:10000].copy() In [21]: df = pd.read_csv(‘train.csv’, index_col=’row_id’) In [22]: df.head() Out[22]: x y accuracy time place_id row_id 0 0.7941 9.0809 54 470702 8523065625 1 5.9567 4.7968 13 186555 1757726713 2 8.3078 7.0407 74 322648 1137537235 3 7.3665 2.5165 […]

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Machine Learning

Evaluating Machine Learning Algorithms

This blog contains notes for me to understand how to evaluate machine learning algorithms . I want to see how models compare and contrast to each other. This is from the following web page: Your First Machine Learning Project in Python Step-By-Step I am evaluating 6 different algorithms in this blog : Logistic Regression (LR) […]

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Kaggle

Tutorial using Kobe Bryant Dataset – Part 4

This part is a Tutorial using Kobe Bryant Dataset – Part 4. You can get the data from https://www.kaggle.com/c/kobe-bryant-shot-selection . What excited me was that this dataset is excellent to practice classification basics, feature engineering, and time series analysis. This is continued from here. Exploring the data In [215]: #Shot accuracy sns.countplot(‘shot_made_flag’,data = data) Out[215]: <matplotlib.axes._subplots.AxesSubplot […]