-
Clone the repo
git clone <SSH>
-
Change to repo's directory
cd Job-Recommender
- Requires python 3.7 or more.
- Install virtualenv using
pip install virtualenv
- Set up python virtual environment for the project, using
virtualenv venv
- Activate the virtualenv.
- If you are using windows then use the command
./venv/scripts/activate
. - If you are using linux systems then use command
source venv/bin/activate
- Download the required dependencies used in the project using,
pip install -r requirements.txt
Congratulations! You have successfully set up the environment!
- The
Datasets/
directory contains all the datasets downloaded for the project. It mainly consists of two job datasets one for IT and other for Non-IT. - Data preprocessing and natural language processing is done in
code/data_cleaning.py
. - Saved cleaned data to a new directory
Cleaned_Datasets/
to save time and reduce redundancy during runtime. - Job recommender is built in
Code/model.py
nltk library used. The following pipeline has been executed.
- remove all non alphabets regex = [^a-zA-Z],
- remove whitespaces
- convert case to lowercase
- tokenize words
- remove stopwords
- stemming
The result saved to
Cleaned_Dataset/
directory.
- First vectorize the text using tfidf vectorizer.
- Then use cosine similarity to find the similarity of text and the one with highest score is the recommended job.
- First copy the contents of your resume or a resume whose job you want to predict and paste the copied contents in
predictor_files/input.txt
. You can also create your own files, sayrandom.txt
in thepredictor_files
directory and paste it in there as well. - Next Open the CLI in repo's home directory.
- cd to
Code/
directory.cd Code
- Run the following command to preprocess the raw data.
python data_cleaning.py
. This will clean the datasets and save them inCleaned_Datasets/
directory. - Run the following command if you want to get top 3 recommendations for the resume you input,
python model.py
.
- You can use the
-f <filename>
or--filename <filename>
options to specify the name of the file in thepredictor_files/
directory that you copied your resume contents to. - The model points to the
predictor_files/
directory by default for accessing your resume contents so if you are creating new file then create and copy in that directory only. - Example command -
python -f example1.txt
,python --filename example1.txt
, note that example1.txt must be inpredictor_files
directory. - Also, you may not be from IT industry only, you may be a doctor, architect, etc, the model takes into consideration by default that you are from IT industry but you can specify that you are from NON-IT field by using the
-o NON-IT
or--option NON-IT
flags. - Example command - to get top 3 recommendations for a NON-IT resume whose contents are pasted in
predictor_files/example2.txt
we use the commandpython model.py -o NON-IT -f example2.txt
- Remember that for theses commands to work your current directory must be
Job-Recommender/Code/
, if its not the same, then cd to it and then use.
cd Code/
python data_cleaning.py
- Copy resume contents to
predictor_files/input.txt
- Run
python model.py