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+{
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 房价预测\n",
+ "\n",
+ "* 杨平\n",
+ "* 2022260288\n",
+ "\n",
+ "\n",
+ "## 任务简介\n",
+ "\n",
+ " 随着社会经济的迅猛发展，人们对住房的需求和期望也越来越高。但是，由于供求关系、政策因素、区域差异等多种因素的影响，房价并不是一个稳定的变量，而是呈现出波动和周期性的特征。因此，如何准确地预测房价的走势，对于各方利益相关者都具有重要意义。\n",
+ "对于房地产开发商而言，房价预测研究可以帮助他们制定合理的开发计划、销售策略和风险控制措施，提高经营效率和盈利能力。对于购房者而言，房价预测研究可以帮助他们了解市场情况、选择合适的时机和区域进行购买或出售，实现资产保值或增值。对于投资者而言，房价预测研究可以帮助他们把握市场机会、优化投资组合、规避风险、获取收益。对于金融市场而言，房价预测研究可以帮助他们监测和评估房地产市场的健康程度、稳定性和影响力，为金融政策制定提供参考依据。\n",
+ "\n",
+ " 综上所述，房价预测研究是一项具有重要社会经济意义的科学活动，在当前国内外都受到广泛关注和重视。房屋的价格和所在区域、楼层、房龄等都有密切的关系，现在需求开发一个房价预测的模型，通过训练数据拟合得到房屋的价格。构建一个回归模型，通过训练数据找到回归模型的参数。利用训练好的模型预测测试数据的房价。数据集含有训练数据16573条，测试数据7104条，数据有十二列：\n",
+ "\n",
+ "|/|/|\n",
+ "|:---:|:---:|\n",
+ "|Direction|房屋所在城市的方位|\n",
+ "|District|区域|\n",
+ "|Elevator|是否有电梯|\n",
+ "|Floor|所在楼层|\n",
+ "|Garden|小区名字|\n",
+ "|Id|唯一的编号|\n",
+ "|Layout|几室，几厅|\n",
+ "|Price|价格（需要预测的值）|\n",
+ "|Region|所在的区域|\n",
+ "|Renovation|房屋装修的类型|\n",
+ "|Size|房屋面积|\n",
+ "|Year|建成年份|\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "## 解决途径\n",
+ "\n",
+ " 首先对数据集进行数据清洗工作：\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " 导入必要的库"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import seaborn as sns\n",
+ "from scipy import stats\n",
+ "import matplotlib.pyplot as plt\n",
+ "import torch.nn as nn\n",
+ "import torch\n",
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "from Train import train_predict"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " 导入数据"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((16560, 12), (7097, 12))"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "data_train = pd.read_csv('train_data.csv')\n",
+ "data_test = pd.read_csv('test_data.csv')\n",
+ "data_train.shape,data_test.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " 将特征划分为数值特征与类别特征"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "numeric:4,category:7\n"
+ ]
+ }
+ ],
+ "source": [
+ "numeric=[f for f in data_train.drop(['Price'], axis= 1).columns if data_train.drop(['Price'], axis= 1).dtypes[f]!='object']\n",
+ "category=[f for f in data_train.drop(['Price'], axis= 1).columns if data_train.drop(['Price'], axis= 1).dtypes[f]=='object']\n",
+ "print('numeric:{},category:{}'.format(len(numeric),len(category)))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " 计算各特征的缺失度"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(16560, 12)"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "missing = data_train.isnull().sum()\n",
+ "missing.sort_values(inplace=True, ascending=False)\n",
+ "missing=missing[missing>0]\n",
+ "data_train.shape\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Total | \n",
+ " Percent | \n",
+ " Types | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Elevator | \n",
+ " 5740 | \n",
+ " 0.346618 | \n",
+ " object | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "