We all are aware of the severeness of Cancer.It is estimated that nearly 18,000 adults die due to Brain Tumor and the survival rate tells us that if detected late then the person dies within the span of 5 years.So, it is necessary that we devise a technique for early detection of the brain tumor and in today's Modern World we have the power of AI to help us in the early diagnosis of these tumors. In today's world with the help of deep learning we can develop a Brain Tumor Detection app which can just by looking at your Brain CT scan would let you know the probability of you having Brain Tumor. While working on cAInvas one of its key features is UseCases Gallary.When working on any of its UseCases you don't have to look for data manually.As they have the feature to import the dataset to your workspace when you work on them.To load the data we just have to enter the following commands: Running the above code in your notebook will load the labelled brain tumor data in your workspace. It is generally suggested that while working on image data it is better to introduce some augmentations in the data like flipping the image, rotating the image by some angles, changing the brightness of the image, etc.Since our deep learning model deals these images in the form of pixel values, so our model thinks these augmented data as new set of data and it improves the performance of the model.For image augmentations we can make a function and pass the image data through it and save them in our directory. As the CT scans contain the images of the brain in the center surrounded by the blank area so in this step we would find the region that contains the image of the brain and crop the rest from the image.For this we would create a function.

Resume Screening is necessary when companies receive thousands of applications for different roles and need to find suitable matches. For this project, the dataset originally consists of 2 columns -- Category and Resume, where the Category denotes the field (eg: Data Science, HR, Testing etc.). By using value_counts on Category, we can find the frequency-wise distribution of different categories present in our dataset. During pre-processing, we need to remove links, hashtags, urls etc. as these are irrelevant in the resume. Further, using nltk, we also remove stopwords (for eg words like'are', 'the', 'or') that provide no significance to the content.

Predict the quantity of fuel consumed during drives. The mileage of a vehicle is defined as the average distance traveled on a specified amount of fuel. But distance is not the only factor that affects fuel consumption. Here, we take into account multiple factors like speed, temperatures inside and outside, AC, and other weather conditions like rain or sun besides distance to predict the consumption of different types of fuels during drives. Predicting the fuel consumption given distance and other factors vice versa (predicting distance given fuel) can prove useful in planning trips as well as performing real-time predictions during driving.

How do we know if a customer is going to shop or walk away? Understanding the customers is crucial to any seller/store/online platform. This understanding can be important in convincing a customer who is just browsing to buy a product. In offline stores, the inferences derived influence the placement of objects in the store. When the same experience is translated to an online store, the sequence of web pages browsed to reach a product becomes important.

According to a report of WHO, around 17.9 million people die each year due to Cardiovascular Diseases.Over the years it has been found that these deaths can be prevented if the diseases are diagnosed at an early stage and even the disease can be cured. Artificial Intelligence has been applied in various fields and one of them is AI for healthcare.We have seen AI practitioners coming up with solution for various disease diagnosis such as Cancer Detection, Detection of Diabetic Retinopathy and much more.The techniques used in these detections mostly involve Deep Learning. So, by combining our knowledge of deep learning and with its integration Iot we can develop a smart digital-stethoscope which can help in diagnosing anomalies in heartbeat in real-time and can help in classifying Cardio-diseases. While working in cAInvas one of its key features is UseCases Gallary.When working on any of its UseCases you don't have to look for data manually.As they have the feature to import your dataset to your workspace when you work on them.To load the data we just have to enter the following commands: As with all unstructured data formats, audio data has a couple of preprocessing steps which have to be followed before it is presented for analysis. Another way of representing audio data is by converting it into a different domain of data representation, namely the frequency domain.

The audio dataset used here is a subset of the Tensorflow speech commands dataset. Each sample is 1-second long mono audio recorded at 8000 Hz. The dataset is a balanced one with 2360 samples in each class. There are many ways to represent audio data, like, waveform, MFCCs, Mel spectrograms, spectrograms and many more. Among them all, the Mel scale is a closer representation of the human audio perception than the standard scale.

Currency notes have identifiers that allow the visually impaired to identify them easily. This is a learned skill. On the other hand, classifying them using images is an easier solution to help the visually impaired identify the currency they are dealing with. Here, we use pictures of different versions of the currency notes taken from different angles, with different backgrounds and covering different proportions. The dataset contains 195 images of 7 categories of Indian Currency Notes -- Tennote, Fiftynote, Twentynote, 2Thousandnote, 2Hundrednote, Hundrednote, 1Hundrednote.