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In this project, we will have some images of aliens and predators and by them, we will train a Convolutional Neural Network using Keras to predict if the image is an alien or predator. The Data contains the following folders:  train: 247 aliens and 247 predatorsvalidation: 100 aliens and 100 predators . Building CNN Let’s import the Keras libraries and packages. from keras.models import Sequential from keras.layers import Convolution2D from keras.layers import MaxPooling2D from keras.layers

In this project, we aim to predict whether the message is spam or ham. we implemented Natural Language Processing, TF-IDF and SVM on Python. The data contains the following columns: Message: text messageCategory: Spam or Ham . let’s get our environment ready with the libraries we’ll need and then import the data! import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline plt.style.use('seaborn-deep') from sklearn.metrics import confusion_matrix

In this project, we used the sales data of mobile phones in various companies. The aim of this project is to find out the relation between features of a mobile phone(eg:- RAM, Internal Memory, etc) and selling price. In addition, predict the price range of the mobile. We are going to use Kernel Support Vector Machine, K-Fold Cross Validation and Grid Search to solve this problem. The data contains the following columns: battery_power: Total energy

In this project, we aim to predict the intention of online shoppers. We implemented the Logistic Regression to create our model and Linear Discriminant Analysis to create dimensionality reduction in the dataset using Python. The data contains the following columns: Administrative: Administrative ValueAdministrative_Duration: Duration in Administrative PageInformational: Informational ValueInformational_Duration: Duration in Informational PageProductRelated: Product Related ValueProductRelated_Duration: Duration in Product Related PageBounceRates: Bounce Rates of a web pageExitRates: Exit rate of a web pagePageValues: Page values of

In this project, we are going to make a Recurrent Neural Network for understanding the reviews of the users and extract the meaningful information behind them to figure out whether the user liked the movie or not. In the other words, the aim of this project is to classify the user movie reviews into positive and negative reviews. we are going to use Recurrent Neural Network using Keras to solve this problem . let’s get

In this project, we are going to implement customer segmentation based on credit card usage behavior with two different approaches (K-means and Hierarchical Clustering) The data contains the following columns: CUST_ID : Identification of credit card holder (Categorical) BALANCE : Balance amount left in their account to make purchases BALANCE_FREQUENCY : How frequently the Balance is updated, score between 0 and 1 (1 = frequently updated, 0 = not frequently updated) PURCHASES : Amount of purchases made from account ONEOFF_PURCHASES : Maximum purchase amount done

In this project, we will have some images of cats and dogs and by them, we will train a Convolutional Neural Network using Keras to predict if the image is a dog or cat. We have 10000 images in total in the dataset which divided to 8000 images for the training set and 2000 images for the test set. . Building CNN Let’s import the Keras libraries and packages. from keras.models import Sequential from keras.layers

In this project, we aim to predict whether the customers will leave the bank or not?. We implemented the Artificial Neural Network (ANN) on Python. The data contains the following columns: RowNumber: The number of rowCustomerId: Identity numberSurname: Last NameCreditScore: The score of credit by bankGeography: Country or regionGender: Male or femaleAge: Customer ageTenure: Year in bankBalance: Amount in accountNumOfProducts: How many accounts, bank account affiliated products the person hasHasCrCard: Do they have credit card or