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    "info": {
        "author": "Devendra Kumar Sahu",
        "author_email": "devsahu99@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
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        "description": "# Hybrid Recommender\n\nThis package usage multiple algorithms and parameters to accomodate different set of use cases.\n\n### Parameters:\n* **item_clusters**: int\n    The number of clusters for item matrix generation. This parameter can be tuned\n* **top_results**: int\n    Number of recommendations needed. Default value is 10\n* **ratings_weightage**: int\n    Weightage for user ratings score. Default is 1\n* **content_weightage**: int\n    Weightage for content score. Default is 1\n* **null_rating_replace**: str\n    Value to be used as replacement for missing ratings. Default is 'mean', other acceptable values are 'zero','one', and 'min'\n\n### Returns:\n    DataFrame having top recommended results for the list of users\n\n### Approach:\n\n1. Create an instance of the hybrid recommender class\n     mr = hybrid_recommender()\n\n2. Call fit method on the defined object by passing on ratings and content data\n     mr.fit(ratings_df,content_df)\n\n3. Call the predict method\n    recommended_df = mr.predict()\n\n------------------------------------------------------------\n\n\n## Example\n\n### Create Ratings DataFrame\n```python\nitem_id = [1,7,9,10,12,2,4,6,8,10,12,3,6,9,12,14,10,13,12,14,11,2,5,7,8,9,10,12]\nuser_id = [1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4,5,5,5,5,5,5,5]\nrating = [4,5,2,3,5,2,3,2,3,4,4,5,1,2,3,1,2,4,5,3,5,3,1,3,5,3,5,3]\nratings = pd.DataFrame({'user_id':user_id,'item_id':item_id,'rating':rating})\n```\n### Create Content DataFrame\n```python\nitems = [1,2,3,4,5,6,7,8,9,10,11,12,13,14]\ncols = ['col1','col2','col3','col4','col5']\nfeats =[[1,0,0,1,1],\n       [1,1,0,0,1],\n       [0,1,1,0,0],\n       [0,1,1,1,0],\n       [1,0,1,1,1],\n       [1,1,1,0,0],\n       [0,1,0,1,0],\n       [0,0,0,1,0],\n       [0,1,1,0,0],\n       [1,1,1,0,1],\n       [0,0,0,1,1],\n       [0,1,0,1,0],\n       [0,1,1,0,1],\n       [0,0,1,1,1],]\nitem_df = pd.DataFrame(feats,index=items,columns=cols)\n```\n**Ratings DataFrame**\n```python\nratings.head()\n```\n**Content DataFrame**\n```python\nitem_df.head()\n```\n### Fitting and prediction\n\n**Creating the recommender object**\n```python\nmy_recommender = hybrid_recommenders(item_clusters=4,top_results=5)\n```\n**Fitting the data**\n```python\nmy_recommender.fit(ratings,item_df)\n```\n**Recommend for few users**\n```python\nmy_recommender.predict([1,2,3])\n```\n**Recommendations for All users**\n```python\nmy_recommender.predict()\n```\n\n",
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        "name": "hybrid-recommender",
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        "summary": "This package will create recommendations based on content as well as user ratings and finally providing top recommendations based on both data points",
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