99-基于Python的京东手机数据分析及预测系统

基于Django的京东手机数据分析及预测系统 - 完整技术实现

一个集数据爬取、分析、机器学习预测、用户管理和推荐系统于一体的电商数据分析平台

📋 目录

• 项目概述
• 技术架构
• 核心功能实现
• 数据模型设计
• 机器学习算法
• 前端界面设计
• 部署与优化
• 项目特色
• 总结与展望

🎯 项目概述

项目背景

随着电商行业的快速发展，数据驱动的决策变得越来越重要。本项目基于京东手机商品数据，构建了一个完整的数据分析和预测系统，为电商运营提供数据支撑。

系统特点

• 数据规模: 1161款手机商品，1982条用户评论，1003条收藏记录
• 算法准确率: 销量预测准确率>85%
• 用户体验: 响应式设计，支持移动端和桌面端
• 技术栈: Django + MySQL + scikit-learn + Bootstrap

💻 项目展示

项目演示视频随后更新于哔哩哔哩个人主页，敬请关注！

🏗️ 技术架构

后端技术栈

# requirements.txt 核心依赖
Django==4.2.20          # Web框架
pymysql==1.1.0          # MySQL驱动
pandas==2.0.3           # 数据处理
scikit-learn==1.3.0     # 机器学习
jieba==0.42.1           # 中文分词
snownlp==0.12.3         # 情感分析
django-simpleui==2023.12.30  # 后台美化
pillow==10.0.1          # 图片处理
faker==19.6.2           # 模拟数据

前端技术栈

<!-- 核心CSS框架 -->
<link href="/static/assets/css/bootstrap.min.css" rel="stylesheet">
<link href="/static/assets/css/app.css" rel="stylesheet">

<!-- 图表库 -->
<script src="/static/assets/plugins/chartjs/js/Chart.min.js"></script>

<!-- 图标库 -->
<link href="/static/assets/css/icons.css" rel="stylesheet">

项目结构

jd_djangoProject/
├── myapp/                      # 主应用模块
│   ├── models.py              # 数据模型定义
│   ├── views.py               # 视图控制器(866行)
│   ├── urls.py                # URL路由配置
│   ├── admin.py               # 后台管理配置
│   ├── recommendation.py      # 推荐系统引擎
│   └── management/            # Django管理命令
├── templates/                  # HTML模板文件
│   ├── base.html              # 基础模板
│   ├── index.html             # 首页模板
│   ├── predict.html           # 销量预测模板
│   └── sentiment_analysis.html # 情感分析模板
├── buildmodel/                 # 机器学习模型
│   ├── build.py               # 模型训练脚本
│   ├── rf_sales_model.joblib  # 随机森林模型
│   └── result3.csv            # 训练数据集(1178条)
├── static/                     # 静态资源文件
├── media/                      # 媒体文件存储
└── speculate.py               # 销量预测接口

🔧 核心功能实现

1. 数据模型设计

# myapp/models.py
from django.db import models
from django.contrib.auth.models import User

class XinXi(models.Model):
    """商品信息模型"""
    title = models.CharField(max_length=500, verbose_name='商品标题')
    price = models.DecimalField(max_digits=10, decimal_places=2, verbose_name='价格')
    brand = models.CharField(max_length=100, verbose_name='品牌')
    score = models.DecimalField(max_digits=3, decimal_places=2, null=True, verbose_name='评分')
    sales = models.IntegerField(null=True, verbose_name='销量')
    
    class Meta:
        verbose_name = '商品信息'
        verbose_name_plural = verbose_name

class Comment(models.Model):
    """评论模型"""
    user = models.ForeignKey(User, on_delete=models.CASCADE, verbose_name='用户')
    product = models.ForeignKey(XinXi, on_delete=models.CASCADE, verbose_name='商品')
    content = models.TextField(verbose_name='评论内容')
    rating = models.IntegerField(choices=[(i, i) for i in range(1, 6)], verbose_name='评分')
    created_at = models.DateTimeField(auto_now_add=True, verbose_name='评论时间')
    
    class Meta:
        verbose_name = '用户评论'
        verbose_name_plural = verbose_name

class Favorite(models.Model):
    """收藏模型"""
    user = models.ForeignKey(User, on_delete=models.CASCADE, verbose_name='用户')
    product = models.ForeignKey(XinXi, on_delete=models.CASCADE, verbose_name='商品')
    created_at = models.DateTimeField(auto_now_add=True, verbose_name='收藏时间')
    
    class Meta:
        verbose_name = '用户收藏'
        verbose_name_plural = verbose_name

2. 视图控制器实现

# myapp/views.py
from django.shortcuts import render, redirect
from django.contrib.auth.decorators import login_required
from django.db.models import Count, Avg
from .models import XinXi, Comment, Favorite
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
import joblib

def index(request):
    """首页视图"""
    # 统计数据
    total_products = XinXi.objects.count()
    total_comments = Comment.objects.count()
    total_favorites = Favorite.objects.count()
    total_users = User.objects.count()
    
    context = {
        'total_products': total_products,
        'total_comments': total_comments,
        'total_favorites': total_favorites,
        'total_users': total_users,
    }
    return render(request, 'index.html', context)

@login_required
def predict_sales(request):
    """销量预测视图"""
    if request.method == 'POST':
        price = float(request.POST.get('price'))
        brand = request.POST.get('brand')
        score = float(request.POST.get('score'))
        
        # 加载训练好的模型
        model = joblib.load('buildmodel/rf_sales_model.joblib')
        
        # 特征工程
        features = np.array([[price, brand_encoding[brand], score]])
        prediction = model.predict(features)[0]
        
        context = {
            'prediction': prediction,
            'features': {'price': price, 'brand': brand, 'score': score}
        }
        return render(request, 'predict.html', context)
    
    return render(request, 'predict.html')

def sentiment_analysis(request):
    """情感分析视图"""
    from snownlp import SnowNLP
    
    comments = Comment.objects.all()
    sentiments = []
    
    for comment in comments:
        s = SnowNLP(comment.content)
        sentiment_score = s.sentiments
        if sentiment_score >= 0.6:
            sentiment = '积极'
        elif sentiment_score >= 0.4:
            sentiment = '中性'
        else:
            sentiment = '消极'
        sentiments.append(sentiment)
    
    # 统计情感分布
    sentiment_counts = pd.Series(sentiments).value_counts()
    
    context = {
        'sentiment_data': sentiment_counts.to_dict(),
        'total_comments': len(comments)
    }
    return render(request, 'sentiment_analysis.html', context)

3. 推荐系统实现

# myapp/recommendation.py
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from .models import User, XinXi, Favorite, Comment

class RecommendationEngine:
    def __init__(self):
        self.user_item_matrix = None
        self.user_similarity = None
        
    def build_user_item_matrix(self):
        """构建用户-商品矩阵"""
        users = User.objects.all()
        products = XinXi.objects.all()
        
        matrix = np.zeros((len(users), len(products)))
        
        for i, user in enumerate(users):
            for j, product in enumerate(products):
                # 基于收藏和评论计算用户对商品的偏好
                favorites = Favorite.objects.filter(user=user, product=product).count()
                comments = Comment.objects.filter(user=user, product=product).count()
                rating = Comment.objects.filter(user=user, product=product).aggregate(Avg('rating'))['rating__avg'] or 0
                
                matrix[i][j] = favorites * 2 + comments + rating
        
        self.user_item_matrix = matrix
        return matrix
    
    def calculate_user_similarity(self):
        """计算用户相似度"""
        if self.user_item_matrix is None:
            self.build_user_item_matrix()
        
        self.user_similarity = cosine_similarity(self.user_item_matrix)
        return self.user_similarity
    
    def get_recommendations_for_user(self, user_id, n=6):
        """为用户生成推荐"""
        if self.user_similarity is None:
            self.calculate_user_similarity()
        
        users = list(User.objects.all())
        user_index = next((i for i, u in enumerate(users) if u.id == user_id), None)
        
        if user_index is None:
            return []
        
        # 找到最相似的用户
        similar_users = np.argsort(self.user_similarity[user_index])[::-1][1:6]
        
        # 基于相似用户的偏好生成推荐
        recommendations = []
        for similar_user_idx in similar_users:
            similar_user = users[similar_user_idx]
            user_favorites = Favorite.objects.filter(user=similar_user)
            
            for favorite in user_favorites:
                if not Favorite.objects.filter(user_id=user_id, product=favorite.product).exists():
                    recommendations.append(favorite.product)
                    if len(recommendations) >= n:
                        break
        
        return recommendations[:n]

# 使用示例
def get_user_recommendations(user_id):
    engine = RecommendationEngine()
    return engine.get_recommendations_for_user(user_id)

🤖 机器学习算法

1. 销量预测模型

# buildmodel/build.py
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import joblib

def build_sales_prediction_model():
    """构建销量预测模型"""
    # 加载数据
    df = pd.read_csv('result3.csv')
    
    # 特征工程
    le_brand = LabelEncoder()
    df['brand_encoded'] = le_brand.fit_transform(df['brand'])
    
    # 特征选择
    features = ['price', 'brand_encoded', 'score']
    X = df[features]
    
    # 销量标签化
    df['sales_category'] = pd.cut(df['sales'], 
                                 bins=[0, 100, 500, 1000, float('inf')],
                                 labels=['低销量', '中等销量', '高销量', '超高销量'])
    
    y = df['sales_category']
    
    # 训练模型
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    
    rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
    rf_model.fit(X_train, y_train)
    
    # 评估模型
    accuracy = rf_model.score(X_test, y_test)
    print(f"模型准确率: {accuracy:.2%}")
    
    # 保存模型
    joblib.dump(rf_model, 'rf_sales_model.joblib')
    
    # 保存标签编码器
    joblib.dump(le_brand, 'brand_encoder.joblib')
    
    return rf_model, accuracy

if __name__ == "__main__":
    model, accuracy = build_sales_prediction_model()
    print(f"销量预测模型训练完成，准确率: {accuracy:.2%}")

2. 情感分析实现

# myapp/sentiment_utils.py
from snownlp import SnowNLP
import jieba
from collections import Counter

def analyze_sentiment(text):
    """情感分析"""
    s = SnowNLP(text)
    sentiment_score = s.sentiments
    
    if sentiment_score >= 0.6:
        return '积极', sentiment_score
    elif sentiment_score >= 0.4:
        return '中性', sentiment_score
    else:
        return '消极', sentiment_score

def generate_wordcloud(comments):
    """生成词云数据"""
    all_text = ' '.join([comment.content for comment in comments])
    
    # 使用jieba分词
    words = jieba.cut(all_text)
    
    # 过滤停用词
    stop_words = {'的', '了', '在', '是', '我', '有', '和', '就', '不', '人', '都', '一', '一个', '上', '也', '很', '到', '说', '要', '去', '你', '会', '着', '没有', '看', '好', '自己', '这'}
    filtered_words = [word for word in words if word not in stop_words and len(word) > 1]
    
    # 统计词频
    word_counts = Counter(filtered_words)
    
    return dict(word_counts.most_common(50))

🎨 前端界面设计

1. 基础模板结构

<!-- templates/base.html -->
<!doctype html>
<html lang="en">
<head>
    <meta charset="utf-8">
    <meta name="viewport" content="width=device-width, initial-scale=1">
    <title>京东商品数据分析可视化系统</title>
    
    <!-- Bootstrap CSS -->
    <link href="/static/assets/css/bootstrap.min.css" rel="stylesheet">
    <link href="/static/assets/css/app.css" rel="stylesheet">
    <link href="/static/assets/css/icons.css" rel="stylesheet">
</head>
<body>
<div class="wrapper">
    <!-- 侧边栏导航 -->
    <div class="sidebar-wrapper" data-simplebar="true">
        <ul class="metismenu" id="menu">
            <li><a href="{% url 'index' %}"><i class='bx bx-home-circle'></i>首页</a></li>
            
            <li class="menu-label">数据信息</li>
            <li><a href="{% url 'ecommerce_products' %}"><i class='bx bx-cart'></i>商品列表</a></li>
            <li><a href="{% url 'ecommerce_comment_list' %}"><i class='bx bx-bookmark-heart'></i>评论列表</a></li>
            
            <li class="menu-label">可视化图表</li>
            <li><a href="{% url 'widgets' %}"><i class='bx bx-cookie'></i>整体分析</a></li>
            <li><a href="{% url 'chart' %}"><i class="bx bx-line-chart"></i>相关性分析</a></li>
            <li><a href="{% url 'comment_chart' %}"><i class="bx bx-comment"></i>评论分析</a></li>
            <li><a href="{% url 'sentiment_analysis' %}"><i class="bx bx-brain"></i>情感分析</a></li>
            
            <li class="menu-label">机器学习算法</li>
            <li><a href="{% url 'predict' %}"><i class="bx bx-lock"></i>销量预测</a></li>
            
            <li class="menu-label">个人中心</li>
            <li><a href="{% url 'recommendations' %}"><i class='bx bx-heart'></i>猜你喜欢</a></li>
            <li><a href="{% url 'favorite_list' %}"><i class='bx bx-bookmark-heart'></i>我的收藏</a></li>
            <li><a href="{% url 'my_comments' %}"><i class='bx bx-comment-detail'></i>我的评论</a></li>
        </ul>
    </div>
    
    <!-- 主要内容区域 -->
    <div class="page-wrapper">
        {% block content %}{% endblock %}
    </div>
</div>

<!-- JavaScript -->
<script src="/static/assets/js/bootstrap.bundle.min.js"></script>
<script src="/static/assets/js/jquery.min.js"></script>
<script src="/static/assets/plugins/chartjs/js/Chart.min.js"></script>
{% block jss %}{% endblock %}
</body>
</html>

2. 数据可视化实现

<!-- templates/widgets.html - 整体分析页面 -->
{% extends 'base.html' %}
{% block content %}
<div class="page-content">
    <div class="row">
        <!-- 统计卡片 -->
        <div class="col-12 col-lg-3">
            <div class="card radius-10">
                <div class="card-body">
                    <div class="d-flex align-items-center">
                        <div class="">
                            <p class="mb-1">商品总数</p>
                            <h4 class="mb-0 text-primary">{{ total_products }}</h4>
                        </div>
                        <div class="ms-auto fs-2 text-primary">
                            <i class="bx bx-cart"></i>
                        </div>
                    </div>
                </div>
            </div>
        </div>
        
        <div class="col-12 col-lg-3">
            <div class="card radius-10">
                <div class="card-body">
                    <div class="d-flex align-items-center">
                        <div class="">
                            <p class="mb-1">用户评论</p>
                            <h4 class="mb-0 text-danger">{{ total_comments }}</h4>
                        </div>
                        <div class="ms-auto fs-2 text-danger">
                            <i class="bx bx-comment"></i>
                        </div>
                    </div>
                </div>
            </div>
        </div>
        
        <div class="col-12 col-lg-3">
            <div class="card radius-10">
                <div class="card-body">
                    <div class="d-flex align-items-center">
                        <div class="">
                            <p class="mb-1">收藏数量</p>
                            <h4 class="mb-0 text-success">{{ total_favorites }}</h4>
                        </div>
                        <div class="ms-auto fs-2 text-success">
                            <i class="bx bx-heart"></i>
                        </div>
                    </div>
                </div>
            </div>
        </div>
        
        <div class="col-12 col-lg-3">
            <div class="card radius-10">
                <div class="card-body">
                    <div class="d-flex align-items-center">
                        <div class="">
                            <p class="mb-1">注册用户</p>
                            <h4 class="mb-0 text-warning">{{ total_users }}</h4>
                        </div>
                        <div class="ms-auto fs-2 text-warning">
                            <i class="bx bx-user"></i>
                        </div>
                    </div>
                </div>
            </div>
        </div>
    </div>
    
    <!-- 图表展示 -->
    <div class="row">
        <div class="col-12 col-lg-6">
            <div class="card radius-10">
                <div class="card-body">
                    <div class="d-flex align-items-center mb-3">
                        <h6 class="mb-0">价格分布</h6>
                    </div>
                    <canvas id="priceChart"></canvas>
                </div>
            </div>
        </div>
        
        <div class="col-12 col-lg-6">
            <div class="card radius-10">
                <div class="card-body">
                    <div class="d-flex align-items-center mb-3">
                        <h6 class="mb-0">品牌分布</h6>
                    </div>
                    <canvas id="brandChart"></canvas>
                </div>
            </div>
        </div>
    </div>
</div>

<script>
// 价格分布图表
const priceCtx = document.getElementById('priceChart').getContext('2d');
const priceChart = new Chart(priceCtx, {
    type: 'bar',
    data: {
        labels: ['0-1000', '1000-2000', '2000-3000', '3000-4000', '4000+'],
        datasets: [{
            label: '商品数量',
            data: {{ price_distribution|safe }},
            backgroundColor: 'rgba(54, 162, 235, 0.2)',
            borderColor: 'rgba(54, 162, 235, 1)',
            borderWidth: 1
        }]
    },
    options: {
        responsive: true,
        scales: {
            y: {
                beginAtZero: true
            }
        }
    }
});

// 品牌分布图表
const brandCtx = document.getElementById('brandChart').getContext('2d');
const brandChart = new Chart(brandCtx, {
    type: 'doughnut',
    data: {
        labels: {{ brand_labels|safe }},
        datasets: [{
            data: {{ brand_data|safe }},
            backgroundColor: [
                '#FF6384', '#36A2EB', '#FFCE56', '#4BC0C0', '#9966FF',
                '#FF9F40', '#FF6384', '#C9CBCF', '#4BC0C0', '#FF6384'
            ]
        }]
    },
    options: {
        responsive: true,
        plugins: {
            legend: {
                position: 'bottom'
            }
        }
    }
});
</script>
{% endblock %}

📊 数据可视化展示

1. 整体数据统计

• 商品总数: 1161款手机产品
• 用户评论: 1982条评论数据
• 收藏记录: 1003条收藏关系
• 注册用户: 22个用户账户

2. 价格分布分析

# 价格区间统计
price_ranges = {
    '0-1000': 0,
    '1000-2000': 0,
    '2000-3000': 0,
    '3000-4000': 0,
    '4000+': 0
}

for product in XinXi.objects.all():
    if product.price <= 1000:
        price_ranges['0-1000'] += 1
    elif product.price <= 2000:
        price_ranges['1000-2000'] += 1
    elif product.price <= 3000:
        price_ranges['2000-3000'] += 1
    elif product.price <= 4000:
        price_ranges['3000-4000'] += 1
    else:
        price_ranges['4000+'] += 1

3. 品牌分布统计

# 品牌统计
brand_stats = XinXi.objects.values('brand').annotate(
    count=Count('id'),
    avg_price=Avg('price'),
    avg_score=Avg('score')
).order_by('-count')

4. 情感分析结果

# 情感分析统计
sentiment_stats = {
    '积极': 0,
    '中性': 0,
    '消极': 0
}

for comment in Comment.objects.all():
    sentiment, score = analyze_sentiment(comment.content)
    sentiment_stats[sentiment] += 1

🚀 部署与优化

1. 环境配置

# 创建虚拟环境
python -m venv venv
source venv/bin/activate  # Linux/Mac
# venv\Scripts\activate  # Windows

# 安装依赖
pip install -r requirements.txt

# 数据库配置
mysql -u root -p
CREATE DATABASE design_99_phone CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;

2. Django设置

# settings.py
DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.mysql',
        'NAME': 'design_99_phone',
        'USER': 'root',
        'PASSWORD': 'your_password',
        'HOST': 'localhost',
        'PORT': '3306',
        'OPTIONS': {
            'charset': 'utf8mb4',
        }
    }
}

# 静态文件配置
STATIC_URL = '/static/'
STATIC_ROOT = os.path.join(BASE_DIR, 'staticfiles')
STATICFILES_DIRS = [
    os.path.join(BASE_DIR, 'static'),
]

# 媒体文件配置
MEDIA_URL = '/media/'
MEDIA_ROOT = os.path.join(BASE_DIR, 'media')

3. 性能优化

# 数据库查询优化
def get_products_with_optimization():
    """优化后的商品查询"""
    return XinXi.objects.select_related().prefetch_related(
        'comment_set', 'favorite_set'
    ).all()

# 缓存配置
CACHES = {
    'default': {
        'BACKEND': 'django.core.cache.backends.redis.RedisCache',
        'LOCATION': 'redis://127.0.0.1:6379/1',
    }
}

# 分页优化
from django.core.paginator import Paginator

def paginate_products(request, products, per_page=20):
    """分页处理"""
    paginator = Paginator(products, per_page)
    page_number = request.GET.get('page')
    page_obj = paginator.get_page(page_number)
    return page_obj

🎯 项目特色

1. 技术亮点

• 完整的电商数据分析流程: 从数据收集到智能分析
• 高准确率机器学习模型: 销量预测准确率>85%
• 中文自然语言处理: 基于jieba和SnowNLP的情感分析
• 个性化推荐系统: 基于协同过滤的智能推荐
• 响应式界面设计: 支持多端访问

2. 数据规模

• 商品数据: 1161款手机产品信息
• 用户数据: 22个注册用户
• 评论数据: 1982条用户评论
• 收藏数据: 1003条收藏记录

3. 算法性能

• 销量预测: 随机森林算法，准确率85%+
• 情感分析: 中文文本情感识别
• 推荐系统: 协同过滤算法
• 词云分析: 基于jieba分词

📈 系统监控

1. 性能指标

# 系统性能监控
import time
from django.db import connection

def performance_monitor(func):
    """性能监控装饰器"""
    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()
        
        # 记录执行时间
        execution_time = end_time - start_time
        
        # 记录数据库查询次数
        query_count = len(connection.queries)
        
        print(f"函数 {func.__name__} 执行时间: {execution_time:.2f}秒")
        print(f"数据库查询次数: {query_count}")
        
        return result
    return wrapper

2. 错误处理

# 全局异常处理
from django.http import JsonResponse
from django.core.exceptions import ObjectDoesNotExist

def handle_exception(func):
    """异常处理装饰器"""
    def wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except ObjectDoesNotExist:
            return JsonResponse({'error': '数据不存在'}, status=404)
        except Exception as e:
            return JsonResponse({'error': str(e)}, status=500)
    return wrapper

🔒 安全特性

1. 用户认证

# 用户认证装饰器
from django.contrib.auth.decorators import login_required
from django.shortcuts import redirect

@login_required
def protected_view(request):
    """需要登录的视图"""
    return render(request, 'protected.html')

# 权限控制
from django.contrib.auth.mixins import LoginRequiredMixin

class ProtectedView(LoginRequiredMixin, View):
    login_url = '/login/'
    redirect_field_name = 'next'

2. 数据验证

# 表单验证
from django import forms

class ProductForm(forms.ModelForm):
    class Meta:
        model = XinXi
        fields = ['title', 'price', 'brand', 'score']
    
    def clean_price(self):
        price = self.cleaned_data['price']
        if price <= 0:
            raise forms.ValidationError('价格必须大于0')
        return price

📝 总结与展望

项目成果

1. 完整的数据分析平台: 集成了数据收集、分析、预测、推荐等完整功能
2. 高准确率预测模型: 销量预测准确率达到85%以上
3. 用户友好的界面: 响应式设计，支持多端访问
4. 可扩展的架构: 模块化设计，便于功能扩展

技术收获

1. Django全栈开发: 掌握了Django框架的完整开发流程
2. 机器学习集成: 学会了在Web应用中集成机器学习算法
3. 数据可视化: 掌握了Chart.js等图表库的使用
4. 中文NLP: 学会了jieba、SnowNLP等中文处理工具

未来改进方向

1. 算法优化: 尝试深度学习模型提升预测准确率
2. 实时数据处理: 集成Kafka等消息队列处理实时数据
3. 微服务架构: 将系统拆分为多个微服务
4. 云原生部署: 使用Docker、Kubernetes进行容器化部署

📞 联系方式

• 联系方式: [码界筑梦坊各平台同名]