Tejas_Makode_LookALike.ipynb

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    {
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     "text": [
      "Lookalike model results saved to 'Lookalike.csv'\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import pandas as pd\n",
    "\n",
    "# Load datasets\n",
    "customers = pd.read_csv('Customers.csv')\n",
    "products = pd.read_csv('Products.csv')\n",
    "transactions = pd.read_csv('Transactions.csv')\n",
    "\n",
    "# Merge datasets for consolidated view\n",
    "merged = transactions.merge(customers, on='CustomerID').merge(products, on='ProductID')\n",
    "\n",
    "# Feature engineering: Create a customer profile\n",
    "customer_profile = merged.groupby('CustomerID').agg({\n",
    "    'TotalValue': 'sum',                  # Total spending\n",
    "    'Quantity': 'sum',                   # Total quantity purchased\n",
    "    'Category': lambda x: ', '.join(x),  # List of categories purchased\n",
    "    'Region': 'first',                   # Customer region\n",
    "}).reset_index()\n",
    "\n",
    "# One-hot encode categories and regions\n",
    "categories_encoded = customer_profile['Category'].str.get_dummies(sep=', ')\n",
    "regions_encoded = pd.get_dummies(customer_profile['Region'], prefix='Region')\n",
    "\n",
    "# Combine features for similarity calculation\n",
    "features = pd.concat([\n",
    "    customer_profile[['TotalValue', 'Quantity']],\n",
    "    categories_encoded,\n",
    "    regions_encoded\n",
    "], axis=1)\n",
    "\n",
    "# Normalize features\n",
    "scaler = StandardScaler()\n",
    "features_normalized = scaler.fit_transform(features)\n",
    "\n",
    "# Calculate pairwise similarity using cosine similarity\n",
    "similarity_matrix = cosine_similarity(features_normalized)\n",
    "\n",
    "# Create a lookalike recommendation for the first 20 customers\n",
    "lookalike_results = {}\n",
    "customer_ids = customer_profile['CustomerID']\n",
    "\n",
    "for i, customer_id in enumerate(customer_ids[:20]):  # First 20 customers\n",
    "    # Get similarity scores for the current customer\n",
    "    similarity_scores = list(enumerate(similarity_matrix[i]))\n",
    "    # Sort by similarity (excluding the customer itself)\n",
    "    similarity_scores = sorted(similarity_scores, key=lambda x: x[1], reverse=True)\n",
    "    # Get top 3 most similar customers\n",
    "    top_3 = [(customer_ids[idx], round(score, 3)) for idx, score in similarity_scores[1:4]]\n",
    "    lookalike_results[customer_id] = top_3\n",
    "\n",
    "# Save to Lookalike.csv\n",
    "lookalike_df = pd.DataFrame({\n",
    "    'CustomerID': lookalike_results.keys(),\n",
    "    'Lookalikes': [str(value) for value in lookalike_results.values()]\n",
    "})\n",
    "lookalike_df.to_csv('Lookalike.csv', index=False)\n",
    "\n",
    "print(\"Lookalike model results saved to 'Lookalike.csv'\")\n"
   ]
  }
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