

In [1]:

    
# Useful starting lines
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
%load_ext autoreload
%autoreload 2

Load the training data into feature matrix, class labels, and event ids:



In [13]:

    
from proj1_helpers import *
DATA_TRAIN_PATH = '' # TODO: download train data and supply path here 
y, tX, ids = load_csv_data(DATA_TRAIN_PATH)

Do your thing crazy machine learning thing here :) ...

Generate predictions and save ouput in csv format for submission:



In [10]:

    
DATA_TEST_PATH = '' # TODO: download train data and supply path here 
_, tX_test, ids_test = load_csv_data(DATA_TEST_PATH)



In [31]:

    
OUTPUT_PATH = '' # TODO: fill in desired name of output file for submission
y_pred = predict_labels(weights, tX_test)
create_csv_submission(ids_test, y_pred, OUTPUT_PATH)



In [ ]:

    
from helpers import sample_data, load_data, standardize

# load data.
height, weight, gender = load_data()

# build sampled x and y.
seed = 1
y = np.expand_dims(gender, axis=1)
X = np.c_[height.reshape(-1), weight.reshape(-1)]
y, X = sample_data(y, X, seed, size_samples=200)
x, mean_x, std_x = standardize(X)