- Linear Regression
- Logistic Regression
- Naive Bayes
- Advance Regression
- Decision Tree
- Random Forset
- Boosting
- K-Means
- PCA
- Regex
- Tf-idf
- POS/CFG Tagger
- HMM/Viterbi model
- Word Embeddings
- LSA
- Word2Vec
- Glove
- Skip-Gram
The objective is to identify the best sectors, countries, and a suitable investment type for making investments. The overall strategy is to invest where others are investing, implying that the 'best' sectors and countries are the ones 'where most investors are investing'.
In this project we will apply techniques learnt in EDA, we will also develop a understanding of risk analytics in banking and financial services and understand how data is used to minimise the risk of losing money while lending to customers.
Like most other lending companies, lending loans to ‘risky’ applicants is the largest source of financial loss (called credit loss). The credit loss is the amount of money lost by the lender when the borrower refuses to pay or runs away with the money owed. In other words, borrowers who default cause the largest amount of loss to the lenders. In this case, the customers labelled as 'charged-off' are the 'defaulters'.
If one is able to identify these risky loan applicants, then such loans can be reduced thereby cutting down the amount of credit loss. Identification of such applicants using EDA is the aim of this project.
We build a model for the price of cars with the available independent variables. It will be used by the management to understand how exactly the prices vary with the independent variables. They can accordingly manipulate the design of the cars, the business strategy etc. to meet certain price levels. Further, the model will be a good way for management to understand the pricing dynamics of a new market.
We build a model for the price of houses with the available independent variables. This model will then be used by the management to understand how exactly the prices vary with the variables. They can accordingly manipulate the strategy of the firm and concentrate on areas that will yield high returns. Further, the model will be a good way for management to understand the pricing dynamics of a new market.
To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.
In this project, we analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.
A conversational bot (chatbot) which can help users discover restaurants across several Indian cities. The main purpose of the bot is to help users discover restaurants quickly and efficiently and to provide a good restaurant discovery experience. The project brief provided to you is as follows.