The iris flower dataset is a classic dataset in machine learning that contains measurements for 150 iris flowers from three different species: Setosa, Versicolor, and Virginica. Each flower is described by four features: sepal length, sepal width, petal length, and petal width. The dataset is commonly used as a benchmark for machine learning algorithms.
To create a model for iris flower classification, we will use a supervised learning approach. Specifically, we will use a classification algorithm called logistic regression. Logistic regression is a simple yet powerful algorithm that is often used for binary classification problems.
Before we can train our model,we need to preprocess the dataset. This involves splitting the dataset into training and testing sets, scaling the features, and encoding the labels. Scaling the features is important because it ensures that each feature is on a similar scale, which can improve the performance of the model. Encoding the labels is neessary because machine learning algorithms require numerical labels.
Once the dataset has been preprocessed, we can train the logistic regression model. During training, the model will learn to map the input features to the correct output labels. We will use a technique called gradient descent to optimize the model parameters and minimize the loss function.
After training the model, we need to evaluate its performance on the testing set. We will use metrics such as accuracy, precision, recall, and F1 score to evaluate the model's performance. These metrics will help us determine how well the model is performing and identify areas where it can be improved
Dataset used :
Unemployment_in_India.csv
Unemployment_Rate_upto_11_2020.csv
Unemployment is measured by the unemployment rate which is the number of people who are unemployed as a percentage of the total labour force.
Estimated Unemployment Rate (%): The estimated unemployment rate is a measure used by governments and economists to assess the percentage of the labor force that is unemployed and actively seeking employment. It is an important economic indicator as it reflects the health of the job market and the overall economy.
Estimated Labour Participation Rate (%) : The labor force participation rate is calculated by dividing the number of people in the labor force (both employed and unemployed individuals) by the total working-age population and then multiplying by 100 to express it as a percentage.