Kaggle：San Francisco Crime Classification

比赛地址
这里用logistic regression来完成这个预测问题。

def read_data(file_name):
    f = open(file_name)
    #ignore header
    f.readline()
    samples = []
    target = []
    for line in f:
        line = line.strip().split(",")
        sample = [float(x) for x in line]
        samples.append(sample)
    return samples

def write_delimited_file(file_path, data,header=None, delimiter=","):
    f_out = open(file_path,"w")
    if header is not None:
        f_out.write(delimiter.join(header) + "\n")
    for line in data:
        if isinstance(line, str):
            f_out.write(line + "\n")
        else:
            f_out.write(delimiter.join(line) + "\n")
    f_out.close()
from sklearn.linear_model import LogisticRegression
import csv_io
import math
import scipy

def train_and_predict():
    #read in the training file
    train = read_data("train.csv")
    print('读取训练数据完毕\n...\n')
    #set the training responses
    target = [x[0] for x in train]
    #set the training features
    train = [x[1:] for x in train]
    #read in the test file
    realtest = read_data("test.csv")
    print('读取待预测数据\n...\n')

    # code for logistic regression
    lr = LogisticRegression()
    lr.fit(train, target)
    print 'Logistic Regression训练完毕!\n...\n'
    predicted_probs = lr.predict_proba(realtest)

    # write solutions to file
    predicted_probs = ["%f" % x[1] for x in predicted_probs]
    write_delimited_file("lr_solution.csv", predicted_probs)

    print('Logistic Regression预测完毕! 请提交lr_solution.csv文件到Kaggle')

if __name__=="__main__":
    train_and_predict()

转自https://blog.csdn.net/han_xiaoyang