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.ipynb_checkpoints/P0. Data preparation-checkpoint.ipynb
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.ipynb_checkpoints/P0. Data preparation-checkpoint.ipynb
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.ipynb_checkpoints/P1. Baseline-checkpoint.ipynb
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.ipynb_checkpoints/P1. Baseline-checkpoint.ipynb
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.ipynb_checkpoints/P2. Evaluation-checkpoint.ipynb
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.ipynb_checkpoints/P2. Evaluation-checkpoint.ipynb
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.ipynb_checkpoints/P3. k-nearest neighbours-checkpoint.ipynb
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.ipynb_checkpoints/P3. k-nearest neighbours-checkpoint.ipynb
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"4 166 346 1 886397596"
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"4 166 346 1 886397596"
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"Name: user, dtype: float64"
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" 18: 'Western'}"
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" 18: 'Western'}"
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"[3 rows x 24 columns]"
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"[3 rows x 24 columns]"
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"4 5 Copycat (1995) Crime, Drama, Thriller"
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"4 5 Copycat (1995) Crime, Drama, Thriller"
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"cell_type": "code",
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"execution_count": 31,
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"execution_count": 16,
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"658 ns ± 16.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
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"471 ns ± 15.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
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"Inefficient way to access items rated by user:\n"
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"Inefficient way to access items rated by user:\n"
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"name": "stdout",
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"text": [
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"67.8 µs ± 1.68 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
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"48.3 µs ± 1.51 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
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"text": [
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"RMSE: 1.5230\n",
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"RMSE: 1.5165\n",
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"MAE: 1.2226\n"
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"MAE: 1.2172\n"
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"data": {
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"data": {
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"text/plain": [
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"1.2226271020019277"
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"1.2172144988785374"
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"\n",
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"\n",
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"sp.accuracy.mae(predictions, verbose=True)"
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"sp.accuracy.mae(predictions, verbose=True)"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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"version": "3.8.8"
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"nbformat": 4,
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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"version": "3.8.8"
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"nbformat": 4,
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