Example input
[Dataset Source]: World Health Organization (WHO)
[Data Variables]: Age, Gender, Country, BMI, Smoking Status, Alcohol Consumption
[Total Observations]: 5000
[Specific Objective]: To understand the factors influencing BMI across different countries.
Example output
Certainly! Let's walk through the process of analyzing the dataset step by step. For this analysis, I'll assume that the dataset is in a tabular format (e.g., CSV or Excel) with columns for Age, Gender, Country, BMI, Smoking Status, and Alcohol Consumption.
### 1. Preprocessing:
#### Load the Dataset:
```python
import pandas as pd
# Assuming the dataset is in a CSV file
df = pd.read_csv('your_dataset.csv')
```
#### Check for Missing Values:
```python
df.isnull().sum()
```
#### Summary Statistics:
```python
df.describe()
```
### 2. Exploratory Data Analysis (EDA):
#### Univariate Analysis:
```python
import seaborn as sns
import matplotlib.pyplot as plt
# Distribution of BMI
sns.histplot(df['BMI'], kde=True)
plt.title('Distribution of BMI')
plt.show()
# Count of observations by Country
sns.countplot(y='Country', data=df)
plt.title('Count of Observations by Country')
plt.show()
```
#### Bivariate Analysis:
```python
# BMI by Gender
sns.boxplot(x='Gender', y='BMI', data=df)
plt.title('BMI by Gender')
plt.show()
# BMI by Smoking Status
sns.boxplot(x='Smoking Status', y='BMI', data=df)
plt.title('BMI by Smoking Status')
plt.show()
```
### 3. Hypothesis Testing:
#### Example: Is there a significant difference in BMI between smokers and non-smokers?
```python
from scipy.stats import ttest_ind
smokers = df[df['Smoking Status'] == 'Smoker']['BMI']
non_smokers = df[df['Smoking Status'] == 'Non-smoker']['BMI']
t_stat, p_value = ttest_ind(smokers, non_smokers)
print(f'T-statistic: {t_stat}, p-value: {p_value}')
```
### 4. Regression Analysis:
#### Example: Predict BMI based on Age, Gender, Smoking Status, and Alcohol Consumption using linear regression.
```python
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
# Prepare data
X = df[['Age', 'Gender', 'Smoking Status', 'Alcohol Consumption']]
X = pd.get_dummies(X, drop_first=True) # Convert categorical variables to dummy/indicator variables
y = df['BMI']
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Build and train the model
model = LinearRegression()
model.fit(X_train, y_train)
# Make predictions
y_pred = model.predict(X_test)
# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
print(f'Mean Squared Error: {mse}')
```
### 5. Data Visualization for Insights:
#### Example: Scatter plot of BMI vs. Age colored by Country
```python
plt.figure(figsize=(12, 8))
sns.scatterplot(x='Age', y='BMI', hue='Country', data=df)
plt.title('BMI vs. Age by Country')
plt.show()
```
### Conclusion:
This is a simplified walkthrough, and you might need to adapt the code based on the actual characteristics of your dataset. Additionally, the choice of analyses and visualizations can vary based on your specific research questions and goals.