Panda Library is used to load dataset in dataframes
Geopy module to calculate the distance
Levenshtein module is used to find the similarity of the words

The program loads the dataset into a pandas dataframe and then loops through all pairs of entries to calculate their distance and name distance. For calculating distance I have use great_circle which is more accurate than geodesic and distance function of the geopy module If the distance is less than 200 meters and the name distance is less than 5, then both the entries are marked similar. The resulting dataframe is then saved to a new csv file with the added 'is_similar' column. Approach or logic behind this program is bruteforce or naive approach. Therefore the Time complexity is O(N*N) where N is the entries. So I have optimize it to carry out computation in less time. Optimized Code is in the second python file.

To optimize it I have used Dictionary to group entries by their rounded latitude and longitude values and then only compaes entries within each grid cell. This reduces the number of pairwise comaprisons with each other. By rounding the latitude and longitude upto one decimal place results into entries which are nearer to each other are grouped into each cell. This reduces the calculaion from entries with each other to entries which are grouped in one cell. By this approach time taken for computation is reduced. Great_circle is used over geodesic and distance of the geopy module for accurate distance.