研究生必备：AI数据分析从入门到精通的完整指南

import pandas as pd

# 读取研究数据
data = pd.read_csv("research_data.csv")

# 数据清洗
cleaned_data = data.dropna()  # 删除缺失值
cleaned_data = cleaned_data[cleaned_data['value'] > 0]  # 过滤异常值

# 数据分析
mean_value = cleaned_data.groupby('group')['value'].mean()  # 按组计算平均值
correlation = cleaned_data[['var1', 'var2']].corr()  # 计算变量间相关系数

# 结果可视化
mean_value.plot(kind='bar')  # 绘制柱状图

# 导入必要库
import pandas as pd
import seaborn as sns
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

# 读取问卷数据
survey_data = pd.read_csv("survey_results.csv")

# 数据预处理
survey_data = pd.get_dummies(survey_data, columns=['gender', 'education'])  # 分类变量编码

# 构建预测模型
X = survey_data.drop('satisfaction', axis=1)  # 自变量
y = survey_data['satisfaction']  # 因变量
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)  # 划分训练集和测试集

# 建立回归模型
model = LinearRegression()
model.fit(X_train, y_train)

# 评估模型
print("模型R²:", model.score(X_test, y_test))

# 可视化关键变量关系
sns.pairplot(survey_data[['age', 'income', 'satisfaction']])

# 导入必要库
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation

# 下载必要资源
nltk.download('punkt')
nltk.download('stopwords')

# 文本预处理函数
def preprocess_text(text):
    tokens = word_tokenize(text.lower())  # 分词并转为小写
    stop_words = set(stopwords.words('english'))  # 停用词列表
    filtered_tokens = [word for word in tokens if word.isalpha() and word not in stop_words]  # 过滤
    return " ".join(filtered_tokens)

# 应用预处理
preprocessed_texts = [preprocess_text(text) for text in raw_texts]

# 创建文档-词项矩阵
vectorizer = CountVectorizer(max_features=1000)
X = vectorizer.fit_transform(preprocessed_texts)

# 主题建模
lda = LatentDirichletAllocation(n_components=5, random_state=42)
lda.fit(X)

# 显示主题
feature_names = vectorizer.get_feature_names()
for topic_idx, topic in enumerate(lda.components_):
    print(f"主题 {topic_idx}:")
    print(" ".join([feature_names[i] for i in topic.argsort()[:-5:-1]]))

# 导入必要库
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
import seaborn as sns

# 读取实验数据
experiment_data = pd.read_csv("experiment_results.csv")

# 数据清洗
experiment_data = experiment_data.dropna()
experiment_data = experiment_data[experiment_data['measurement'] < experiment_data['measurement'].quantile(0.99)]  # 移除异常值

# 特征工程
experiment_data['temp_squared'] = experiment_data['temperature'] ** 2  # 创建二次项特征
experiment_data['interaction'] = experiment_data['temperature'] * experiment_data['pressure']  # 创建交互项特征

# 准备建模数据
X = experiment_data[['temperature', 'pressure', 'concentration', 'temp_squared', 'interaction']]
y = experiment_data['measurement']

# 构建随机森林模型
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X, y)

# 评估模型
predictions = model.predict(X)
print("模型RMSE:", np.sqrt(mean_squared_error(y, predictions)))

# 特征重要性可视化
importance = model.feature_importances_
features = X.columns
plt.figure(figsize=(10, 6))
sns.barplot(x=importance, y=features)
plt.title('特征重要性')
plt.show()