In order to use our Yosnalab shopping services, you need to read carefully about our terms and conditions. If You disagree any part of terms then you cannot access our service.
I agree to pay the rental rate for the period we used the product and in transit and further agrees to promptly return the products at the end of the rental period in the same condition as received.
I will give alert to Yosnalab if my contact information changes.
I also agree to pay for any damages to, or loss of, the rented merchandise occurring during their time of possession or because of loss and or damage of the products. Upon return and inspection if any and all repairs necessary and or accessories missing that were itemized will be charged at our current rates and billed to you.
A full day rental is charged, even for a partial day use. There are absolutely no refunds for early returns.
The products can be used upto 4 weeks only, Cost will be calculated as per day of usage. We are not responsible for the damage of materials or other liability of any kind resulting from the use or malfunction of the equipment. We will not return the initial deposit of money until the product is returned back.
I am responsible for keeping track of my due dates. I understand that any notices sent out by Yosnalab are a courtesy only and failure to receive them does not excuse me from any charges.
A copy of both sides of their valid institution identification card need to be present. Depositing the money can be done only in the form of cash, not through credit/debit cards.
If you have questions or suggestions, please contact us.
Internet is frequently used for online shopping. Online shopping means the shopping behavior of consumers in an online store or a website used for online purchasing purpose. Online shopping is the activity or action of buying products or using services over the Internet. It means shopping online, landing on a seller’s website, selecting something, and arranging for its delivery. The buyer either pays for the goods or service online with a credit or debit card or select cash on delivery option. Online shopping has experienced a rapid growth during the past years due to its unique advantages for both consumers and retailers, which includes shopping at round the clock facilities, decreasing the frequency to store visits, saving travel costs, increasing market area, decreasing overhead expenses and offering a wide range of products.
More than 85% of the world’s online population has ordered goods over the internet during the recent years. Predicting and analyzing users' intentions for a particular product, or field, based on activities within a website is crucial for e-commerce websites and ad display networks, mostly for retargeting. By keeping records of the search patterns of the consumers, online merchants can have a clear understanding of their behaviors and intentions. In mobile e-commerce a good set of data is available and potential consumers browse for product details before making a purchase, thus reflecting consumer’s purchase intentions. Users always show different search patterns, i.e, time spent per item, search frequency and returning visits.
Why: Problem statement
When shopping in a store, suppose if the required item is not available, then they will walk in for another shop and so on. But when users prefer online shopping, it allows people to browse through a large selection of products and they can buy whatever they want by comparing the quality of the products, price of the products, etc., through the internet. We design a robust classifier to predict and analyze buying intentions based on user behavior pattern within a very large e-commerce website. The aim of this project is to find out the activity patterns of users that lead to buy sessions and then extrapolate as templates to predict a high probability of purchase in associated websites. The data used for analysis consists of nearly about 1 million sessions containing the click data of users. But, only 3% of the training data consists of buy sessions - so making it a very unbalanced dataset.
How: Solution description
Clustering is a Machine Learning technique that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group. In theory, data points that are in the same group should have similar properties and/or features, while data points in different groups should have highly dissimilar properties and/or features. Clustering is a method of unsupervised learning and is a common technique for statistical data analysis used in many fields. While training the model, I got the accuracy of 1.0 .
Most used Clustering Algorithms
K-means clustering is a simple unsupervised learning algorithm that is used to solve clustering problems. It follows a simple procedure of classifying a given data set into a number of clusters, defined by the letter "k," which is fixed beforehand.
The clusters are then positioned as points and all observations or data points are associated with the nearest cluster, computed, adjusted and then the process starts overusing the new adjustments until a desired result is reached. K-means clustering has uses in search engines, market segmentation, statistics, and even astronomy.
Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that groups similar objects into groups called clusters. The endpoint is a set of clusters, where each cluster is distinct from each other cluster, and the objects within each cluster are broadly similar to each other.
How is it different from competition
In this project, we discuss and compare traditional machine learning techniques with the most advanced deep learning approaches. We show that both Deep Belief Networks and Stacked Denoising auto-Encoders achieved a considerable substantial improvement by extracting features from high dimensional data during the pre-train phase. They also prove to be more convenient to affect with severe class imbalance. The advantage of probabilistic generative models inspired by deep neural networks is that they will mimic the method of a consumer’s purchase behavior and capture the latent variables to explain the data.
Who are your customers
Online merchants, retailers, wholesalers, and e-commerce merchants