在Django中使用机器学习：实现推荐系统，分类器等等

import numpy as np
import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity

def recommend(user):
    # 读取数据
    data = pd.read_csv('data.csv')
    # 处理数据，转化成用户-物品矩阵
    user_item_matrix = pd.pivot_table(data, values='rating', index=['userId'], columns=['itemId'])
    user_item_matrix = user_item_matrix.fillna(0)
    # 计算余弦相似度，得到相似度矩阵
    similarity_matrix = cosine_similarity(user_item_matrix)
    user_index = data.loc[data['userId'] == user].index[0]
    # 找到相似度最高的前N个用户
    topN_users = similarity_matrix[user_index].argsort()[:-N-1:-1]
    # 找到这些用户评价过的物品，并计算他们的评价得分
    items = []
    for idx in topN_users:
        items += list(user_item_matrix.iloc[idx].nonzero()[0])
    items = list(set(items))
    scores = []
    for idx in items:
        score = 0
        for i in topN_users:
            if user_item_matrix.iloc[i][idx] != 0:
                score += similarity_matrix[user_index][i] * user_item_matrix.iloc[i][idx]
        scores.append((idx, score))
    # 按照得分从高到低排序，得到推荐物品
    scores = sorted(scores, key=lambda x: x[1], reverse=True)
    recommend_items = [data.loc[data['itemId'] == i]['item'].values[0] for i, _ in scores[:N]]
    return recommend_items

import numpy as np
import pandas as pd
from sklearn.naive_bayes import GaussianNB

def train_classifier():
    # 读取和处理数据
    data = pd.read_csv('data.csv')
    X = data.iloc[:, :-1].values
    y = data.iloc[:, -1].values
    # 训练模型
    classifier = GaussianNB()
    classifier.fit(X, y)
    return classifier

def predict(classifier, new_data):
    # 预测分类
    return classifier.predict(np.array(new_data).reshape(1, -1))[0]