Problem Statement being an information scientist for the marketing division at reddit.

Problem Statement being an information scientist for the marketing division at reddit.

i have to discover the many predictive key words and/or expressions to accurately classify the the dating advice and relationship advice subreddit pages them to determine which advertisements should populate on each page so we can use. Because this is a category issue, we’ll utilize Logistic Regression & Bayes models. Misclassifications in this case will be fairly benign therefore I will utilize the precision rating and set up a baseline of 63.3per cent to price success. Making use of TFiDfVectorization, I’ll get the function value to ascertain which terms have the greatest forecast energy for the goal factors. If effective, this model is also utilized to a target other pages which have comparable regularity for the words that are same expressions.

Data Collection

See relationship-advice-scrape and dating-advice-scrape notebooks with this component.

After switching most of the scrapes into DataFrames, they were saved by me as csvs that you can get within the dataset folder for this repo.

Information Cleaning and EDA

  • dropped rows with null self text column becuase those rows are worthless in my experience.
  • combined name and selftext column directly into one brand new columns that are all_text
  • exambined distributions of term counts for titles and selftext column per post and contrasted the 2 subreddit pages.

Preprocessing and Modeling

Found the baseline precision rating 0.633 this means if i usually find the value that develops oftentimes, i will be appropriate 63.3% of times.

First effort: logistic regression model with default CountVectorizer paramaters. train rating: 99 | test 75 | cross val 74 Second attempt: tried CountVectorizer with Stemmatizer preprocessing on first pair of scraping, pretty bad rating with a high variance. Train 99%, test 72%

  • attempted to decrease maximum features and rating got a whole lot worse
  • tried with lemmatizer preprocessing instead and test score went as much as 74per cent

Cancelling an online payday loan. Cancelling a quick payday loan – what you need to understand

Cancelling an online payday loan. Cancelling a quick payday loan – what you need to understand

Cancelling a quick payday loan – Interactive

You’d a unanticipated cost, and needed money fast. In a panic, you went along to a payday lender and took down that loan. Now your buddy has agreed to spot you the income alternatively. You would like you’d never ever taken out that pay day loan.

Driven to your Poorhouse: How automobile Title Lenders Prey on People in the us

Driven to your Poorhouse: How automobile Title Lenders Prey on People in the us

The cheerful come-ons appear more cheesy than sleazy — “Looking for an alternative way to Borrow? ” “Apply Now-Get Cash Today! ” “Go From $0 to Cash in under an Hour” — but they are maybe not the friendly provides of local diversified banking institutions. These are the insidious pitches of businesses which do one thing very well: make car title loans to Us citizens in need of money.

Car-dependent transportation systems produce the environment that is perfect vehicle name loan providers to feed down low-income People in the us.

These extremely specific loan providers do a gangbuster company, attracting vast sums of dollars in loan payments yearly. Nevertheless, the no-savings-just-loans clothes are little proven to most middle- and upper-income families. That’s because their business design requires starting tens and thousands of storefronts in poorer communities, and tossing up websites on the web, to focus on families who require money but whose only significant asset is a car or truck, ordinarily a high-mileage beater. They offer their clients interest that is high loans against some part of the worth of the automobiles, frequently without having a credit or income check. And so they make those loans at unconscionable prices that will strike 600 % for a yearly basis.

Difficult to think, nonetheless it gets far worse.