Machine Learning Excel

Master ML basics with regression, clustering, and predictions in Excel

Blocco 1 di 10
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Dataset Records
Total data points loaded
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Model Accuracy
Best performing model
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Features
Input variables analyzed
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Predictions Made
Total forecasts generated

Blocco 1: Data Cleaning

Clean and prepare your data for machine learning analysis

Microsoft Excel - Data Cleaning

Sales Dataset

Customer sales data with revenue, products, and demographics

Marketing Dataset

Marketing campaign data with conversions and customer behavior

Financial Dataset

Financial market data with prices, volumes, and indicators

Custom Dataset

Upload your own dataset for analysis

Import Data

Load dataset from CSV or Excel

Inspect

Examine data structure and quality

Clean

Handle missing values and outliers

Validate

Verify data quality and consistency

Column Data Type Missing Values Quality Actions
Age Numeric 3 (2%) Good
Income Numeric 0 (0%) Excellent
Category Text 12 (8%) Poor

Blocco 2: Data Exploration

Explore and understand your dataset with statistical analysis

Microsoft Excel - Data Exploration

Statistical Summary

Descriptive statistics for all variables

Distribution Analysis

Histograms and distribution plots

Variable Relationships

Scatter plots and correlation analysis

Data Distribution

Feature Correlations

Variable Mean Median Std Dev Min Max
Age 34.2 32.0 12.4 18 65
Income 45,230 42,000 18,500 25,000 120,000
Purchase Amount 234.50 180.00 156.30 15.50 1,200.00

Feature Analysis

Age
0.85
Income
0.92
Location
0.67
Category
0.73

Blocco 3: Correlation Analysis

Discover relationships between variables and feature dependencies

Microsoft Excel - Correlation Analysis

Correlation Methods

Pearson

Linear relationships

Spearman

Monotonic relationships

Kendall

Rank correlations

Variable Selection







Correlation Heatmap

Variable Pair Correlation Coefficient P-Value Significance Interpretation
Age ↔ Purchase Amount 0.73 < 0.001 *** Strong positive correlation
Income ↔ Purchase Amount 0.84 < 0.001 *** Very strong positive correlation
Gender ↔ Category 0.23 0.045 * Weak positive correlation
Age ↔ Income 0.45 < 0.01 ** Moderate positive correlation

Correlation Insights

Strongest Correlation
0.84
Significant Pairs
12
Multicollinearity Risk
Low
Feature Selection
8/10

Blocco 4: Regression Analysis

Build regression models to predict continuous variables

Microsoft Excel - Regression Analysis

Linear Regression

Simple linear relationships between variables

Multiple Regression

Multiple independent variables predicting one target

Polynomial Regression

Non-linear relationships with polynomial features






80% Train / 20% Test

Regression Plot

Regression Results

R-Squared
0.76
Adjusted R²
0.73
RMSE
124.5
MAE
89.3
Variable Coefficient Std Error t-statistic P-value Significance
Intercept 145.20 23.45 6.19 < 0.001 ***
Marketing Spend 2.34 0.45 5.20 < 0.001 ***
Product Quality 78.90 12.30 6.41 < 0.001 ***

Blocco 5: Clustering Analysis

Discover hidden patterns and group similar data points

Microsoft Excel - Clustering Analysis

K-Means

Partition into K clusters

Hierarchical

Tree-based clustering

DBSCAN

Density-based clustering

4 clusters




Cluster Visualization

Elbow Method

Clustering Results

Silhouette Score
0.68
Inertia (WCSS)
2,450
Optimal K
4
Iterations
12
Cluster Size Avg Income Avg Spending Description
Cluster 0 45 (23%) €32,000 €8,500 Low income, low spending
Cluster 1 38 (19%) €65,000 €12,000 High income, moderate spending
Cluster 2 52 (26%) €45,000 €22,000 Moderate income, high spending
Cluster 3 65 (32%) €78,000 €35,000 High income, high spending

Blocco 6: Decision Tree

Build interpretable decision tree models for classification

Microsoft Excel - Decision Tree

Classification Tree

Predict categorical outcomes (Yes/No, High/Medium/Low)

Regression Tree

Predict continuous numeric values

5 levels
20 samples

Decision Tree Structure

📊 Age ≤ 35?
├─ 👍 Yes: Income ≤ €40k?
│ ├─ 👍 Yes: No Purchase (85%)
│ └─ 👎 No: Category = Electronics?
│ ├─ 👍 Yes: Purchase (78%)
│ └─ 👎 No: Maybe (45%)
└─ 👎 No: Income ≤ €60k?
├─ 👍 Yes: Maybe (62%)
└─ 👎 No: Purchase (91%)

Decision Tree Performance

Accuracy
0.84
Precision
0.79
Recall
0.81
F1-Score
0.80

Feature Importance in Decision Tree

Age
0.95
Income
0.87
Category
0.62
Location
0.34

Blocco 7: Model Validation

Validate and test your machine learning models rigorously

Microsoft Excel - Model Validation

Hold-out Validation

Split data into training and testing sets

K-Fold Cross Validation

Multiple train/test splits for robust evaluation

Bootstrap Validation

Random sampling with replacement

Learning Curves

Validation Curves

Cross-Validation Results

Fold Training Accuracy Validation Accuracy Training Loss Validation Loss
Fold 1 0.87 0.82 0.23 0.31
Fold 2 0.89 0.85 0.19 0.28
Fold 3 0.86 0.83 0.25 0.30
Fold 4 0.88 0.81 0.21 0.33
Fold 5 0.90 0.84 0.18 0.29
Average 0.88 ± 0.015 0.83 ± 0.015 0.21 ± 0.028 0.30 ± 0.019
Cross-Val Score
0.83 ± 0.02
Overfitting Risk
Low
Generalization
Good
Model Stability
High






Blocco 8: Predictions

Make predictions using your trained machine learning models

Microsoft Excel - Predictions

🔮 Prediction Interface

Enter new data points to get predictions from your trained model

Prediction Confidence

Feature Impact

Batch Predictions

Customer ID Prediction Confidence Key Factors Risk Level
C001 78.5% Purchase High Age, Income Low
C002 23.1% Purchase Medium Category, Season Medium
C003 91.2% Purchase Very High Income, History Low

Single Prediction

Predict outcome for one data point

Batch Predictions

Process multiple predictions at once

Real-time API

Set up live prediction service

Blocco 9: Data Visualization

Create compelling visualizations to communicate your ML insights

Microsoft Excel - Data Visualization

Scatter Plots

Show relationships between variables

Correlation Heatmap

Visualize correlation matrices

Distributions

Histograms and density plots

Model Performance

ROC curves, confusion matrices

Feature Relationships

Model Performance Metrics

Prediction Distribution

ROC Curve

Visualization Controls

Interactive Dashboard Elements

KPI Cards

Key performance indicators

Interactive Filters

Dynamic data filtering controls

Data Tables

Sortable and searchable tables

Blocco 10: ML Project Report

Create a comprehensive machine learning project report

Microsoft Excel - ML Report

Executive Summary

High-level business insights and recommendations

Technical Report

Detailed methodology and implementation

Presentation

Stakeholder presentation format

Project Summary

Best Model
Decision Tree
Final Accuracy
84.2%
Key Features
Age, Income
Business Impact
High

Model Comparison Summary

Section Content Status Key Insights
Data Quality 1,500 records, 8 features Complete High quality, minimal missing values
Exploration Statistical analysis, correlations Complete Strong age-income correlation (0.84)
Models Tested Linear Regression, Decision Tree, K-Means Complete Decision Tree performed best (84.2%)
Validation 5-fold cross-validation Complete Consistent performance across folds
Predictions 150 test predictions Complete High confidence predictions (92% avg)
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Certificato di Completamento

Machine Learning Master

Hai completato con successo tutti i 10 blocchi del corso Machine Learning!

Competenze ML Acquisite:
Data Cleaning • Exploratory Analysis • Correlation Analysis • Regression Models • Clustering Algorithms • Decision Trees • Model Validation • Predictions • Data Visualization • ML Reporting

Final Model Accuracy: 84.2%
Didattica.live - AI & Machine Learning Excellence