Movie Recommendation SystemLast Updated on May 3, 2021
The main goal of this machine learning project is to build a recommendation engine that recommends movies to users. This python project is designed as an Item Based Collaborative Filter.
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Convolutional Neural Network Application To Classify Identical Twins(December 2019-March 2020) (Python,Ml)Last Updated on May 3, 2021
-Transfer learning is employed to classify the images of identical twins.
-Transfer learning basically means using the knowledge acquired during a previous problem solving for finding a solution to a new problem in hand.
-Weights and biases values are used with some fine tuning.
-Pre-trained Convolutional Neural Network models which are developed for image recognition tasks on ImageNet data set, provided by Keras API is used as feature extractor preprocessor.
-All these models are proven to be very efficient in image recognition task.They have showcased very high accuracy on ImageNet dataset.The labels they have given for images were highly accurate with very less error percentage.
-Only convolutional base layers are used here,that is fully connected layers are not included here.
-Fully connected layer is not included and a new fully connected layer is addee at the end for the required categorisation of the data.
-During dataset building-collected images of the identical twins.Two categoried were defined in this way.
-VGG19 is used as a standalone program to extract features from the dataset.
-Feature vectors for training dataset is obtained and mean feature vector for both categories were calculated.
-Testing is done by comparing the feature vectors of testing data with mean feature vectors of each category using cosine similarity.
-Obtained a fair accuracy while testing.
Churn PredictionLast Updated on May 3, 2021
Predicting Customer Churn at a Fictitious Wireless Telecom Company
Churn Management has gotten great attention among the telecommunication Industry because it is proved that instead of going for advertisements to find new customers it’s better to find a technique, solution, and all the available resources in our service to figure out a pattern to make customers stay in the company. Every telecommunication company has huge competition and due to easy access of the plans and services provided by all the companies, a customer can switch the company anytime. For churn Prediction, it is most required to identify the customer who has the highest probability of leaving the service of the company and it will be effective if it’s done at the right time. Through this company can make a decision on what service to provide to make the customer not leave the service.
Here, we can reformulate the given problem as a Classification problem. My goal is to build a Classification model that can predict if Customers will stay with the company or not from the given features. To achieve this, first, I did data analysis and data cleaning, data preparation for training, and then model building. After this, based on the performance I find the best parameters of our model through GridSearchCV which best suits for the given data and gave the expected result.
Dog And Cat Image ClassificationLast Updated on May 3, 2021
Dog and cat image classification
The project classifies an image into a dog or a cat. The model has been built by using Convolutional Neural Network or also known as CNN. CNN is a part of deep learning which deals with analysing images. It is widely used for image recognition and classification. This project was developed by using Python. Python is an interpreted, high-level and general-purpose programming language. Python was implemented on Jupyter Notebook.
Libraries and Functions used-
Various Python libraries were used while developing the ML model. The tools used were:
1. tensorflow- It focusses on training of neural networks
2. load_model- This library is used to load a model and construct it identically
3. tkinter- It is a python GUI toolkit
4. PIL- It is Python Image Library that supports in doing operations with images
5. Filedialog- It is used for selecting a file/directory
6. Playsound- It is used for playing audios
7. ImageDataGenerator- It is a class of Keras library used for real-time data augmentation
8. Flow_from_directory- It is an image augmentation tool
9. keras Preprocessing- It is the data preprocessing module of keras which provides utilites for working with image data.
10. load_img- It loads the image in PIL format.
11. img_to_array- It changes the image into a numpy array.
12. expand_dim- It expands the dimension to add an extra dimension for a batch of only one image with axis=0.
In this neural network 2 activation functions were used-
The methods followed were:
1. Pre-processing of data
1.1 Training data
1.2 Testing data
2. Building CNN
2.1 Adding the first convolution layer
2.3 Adding the second convolution layer
2.5 Full connection
2.6 Output layer
The accuracy of last(50) epoch was 97%
This function loads the ML model and take the image input given by the user and then pre-process it. Later the pre-processed image goes as an input to ML model which gives the prediction. For our output, this code plays a sound corresponding to the prediction.
The final page asks the user to select an image from the local computer. The tab’s name is ‘Image Classifier’.
Once the user selects the image, the model successfully predicts whether the image is of a dog or a cat. The model also plays a sound stating about the prediction.
ClassificationLast Updated on May 3, 2021
What is classification?
In machine learning, classification refers to a predictive modeling problem where a class label is predicted for a given example of input data. Examples of classification problems include: Given an example, classify if it is spam or not. Given a handwritten character, classify it as one of the known characters.
Types of Classification
}3.1 Logistic Regression
}3.2 K-Nearest Neighbors (K-NN)
}3.3 Support Vector Machine
}3.4 kernel svm
}3.5 Naïve bayes
}3.6 Decision tree classification
}3.7 Random forest classification
Table of contents
}Importing the libraries
}Importing the dataset
}Splitting the dataset into the Training set and Test set
}Training the model on the Training set
}Predicting a new result
}Predicting the Test set results
} Making the Confusion Matrix
}Visualizing the Training set results
}Visualizing the Test set results
}Problem description: A car company is releasing a new suv car model . we are given a dataset of 400 outcomes with customer’s age , salary and whether they have purchased it before or not I have to predict which customer is going to buy that suv .
RESULT FOR ALL:
K-Nearest Neighbors (K-NN)
Support Vector Machine