Image classification is a process to classify the image within different classes. Image classification is one of the most important applications of deep learning. Transfer Learning is a highly efficient technique for image classification problems. Basically, in the transfer learning, we have to use “ImageNet” dataset weights to improve the prediction accuracy. To prove my points here I use the scene-15 dataset. You can found this dataset, here.

Scene-15

Some pictures from the scene-15 dataset

The Scene-15 dataset contains different pictures with 15 different classes as shown above. A total of 4485 images are available in 15 different classes among which we are going to use 100…

What is the first thing that we need to see in the dataset? It is to find that there are any missing or negative values available in the dataset or not. Here, we will first locate the missing value.

Missing values

From the above table, we can clearly see that there are many missing values are available in the dataset. So we will learn about how to solve it without affecting other values of the dataset.

The first thing we have to replace with numpy’s ‘NaN’ value.

What is a decision tree?

First we need to understand that what does decision tree means. It means that it is a kind of supportive tool which helps us to understand the possible consequences through tree-like model.

Why the decision tree needed?

As it gives tree-like model it descibes every possible solution in algorithm which can easily understandable. It provides very effective and deep knowledge about the situation. That's how decision tree helps in ML.
  • In our case, I used the diabetes database which contains information about Pregnancies, Glucose level, blood pressure, Skin Thickness, Insulin, BMI, Age, DiabetesPedigreeFunction for more than 700 patients and its outcome.
  • First, we need to…

In this post, we will perform data analysis in python on the Boston house price dataset. Before getting started it is inevitable to understand the data. So, Let’s understand the data first.

 - CRIM per capita crime rate by town - ZN proportion of residential land zoned for lots over 25,000 sq.ft. - INDUS proportion of non-retail business acres per town - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) - NOX nitric oxides concentration (parts per 10 million) - RM average number of rooms per dwelling - AGE proportion of owner-occupied units built…

Utsav Jivani

Currently, pursuing a master’s in computer science from Lakehead University. Deep learning is my profession.

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