Machine Learning Classification Data Visualization Modelling

• Importing Data
• Analysing Data
• Data Cleaning
  • Cleaning Outliers
  • Replacing Outliers with Bounds
  • Checking Boxplots after Removing Outliers
• Data Pre-Processing
  • Encoding Class
• Machine Learning
  • Splitting Data
  • Standardizing Data
  • Training Different Models
• Results and Conclusion
  • Model Performance Comparison
  • Conclusion
• THANK YOU

We begin by importing necessary libraries and loading our dataset.

We inspect the dataset by checking its dimensions, information, and statistical summaries. Visualizations like KDE plots, pairplots, and heatmaps help us understand the data's distribution and correlations.

We address data cleaning tasks such as handling outliers and replacing null values.

1. Cleaning Outliers:
   We identify and replace outliers in the dataset to ensure they don't skew our analysis or modeling.

2. Replacing Outliers with Bounds:
   Outliers are replaced with upper and lower bounds to bring them within an acceptable range.

3. Checking Boxplots after Removing Outliers:
   We visualize boxplots post outlier removal to confirm that the data is now cleaned.

We prepare the data for modeling tasks by encoding categorical features and splitting it into training and testing sets.

1. Encoding Class:
   We use label encoding to transform the target variable into numerical format.

We experiment with various machine learning models to identify the best performer for our dataset.

1. Splitting Data:
   We split the dataset into training and testing sets to facilitate model training and evaluation.

2. Standardizing Data:
   Standardization is applied to ensure all features have a similar scale, which aids model performance.

3. Training Different Models
   Several machine learning classifiers are trained and compared based on their accuracy scores.

We summarize the performance of each model and select the best-performing one for predicting raisin varieties.

• Model Performance Comparison

An overview of the accuracy scores of different machine learning models.

Based on the evaluation metrics, the best-performing classifier is identified and can be used for further analysis or deployment.