Using a dataset comprised of songs of two music genres (Hip-Hop and Rock), I trained a classifier to distinguish between the two genres based only on track information derived from Echonest (now part of Spotify). I first made use of pandas and seaborn packages in Python for subsetting the data, aggregating information, and creating plots when exploring the data for obvious trends or factors.
Next, I used the scikit-learn package to predict whether it correctly classifies a song's genre based on features such as danceability, energy, acousticness, tempo, etc. I implemented some of the common algorithms such as PCA, logistic regression, and decision trees.
I have used two different datasets (courtesy of Datacamp).
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‘echonest-metrics.json’ contains data that has music features of each track such as danceability, energy, acousticness, tempo, etc on a scale from -1 to 1.
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‘fma-rock-vs-hiphop.csv’ contains extensive data about the tracks and their genre classification (rock or hip-hop).
Business Question that I try to answer: "Predicting ?"
These are some of the key findings for this project:
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The features .
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The probable features in a typical credit card application are Gender, Age, Debt, Married, BankCustomer, EducationLevel, Ethnicity, YearsEmployed, PriorDefault, Employed, CreditScore, DriversLicense, Citizen, ZipCode, Income and finally the ApprovalStatus.
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Features like DriversLicense and ZipCode are not as important as the other features in the dataset for predicting credit card approvals, so they are dropped to obtain a more streamlined training.
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Our numeric and non-numeric data (specifically data that are of float64, int64, and object types). Specifically, the features 2, 7, 10 and 14 contain numeric values (of types float64, float64, int64 and int64 respectively) and all the other features contain non-numeric values.